{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CNN2b.ipynb","provenance":[{"file_id":"11CKvMWbYn2HYpLAfu2xRhNmw9HDnkG5B","timestamp":1575826262900},{"file_id":"1nbofAi2dLnhJvkcLl1Xag7I1vx4MPR2s","timestamp":1575818931262},{"file_id":"1tTQLWB1ve20LC-Cxir5CrLKQ_vDyzeI2","timestamp":1575815207334}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"SddPvQpMTD0g","colab_type":"text"},"source":["# CNN2b SETTINGS\n","\n","SUBJECT'S DATA SELECTION:                                                                                                  \n","- **A**: Signal samples: 7794, EEG channels: 64, Testset letters: 100                                                           \n","- **B**: Signal samples: 7794, EEG channels: 64, Testset letters: 100 "]},{"cell_type":"code","metadata":{"id":"9nw7AE3rLWlk","colab_type":"code","outputId":"614093d6-a8a3-45d7-9835-17cd09f11f14","executionInfo":{"status":"ok","timestamp":1576425851495,"user_tz":-60,"elapsed":27553,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":333}},"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","import numpy as np     \n","import random           \n","from scipy.io import loadmat\n","from scipy import signal\n","from google.colab import drive\n","import warnings\n","import string\n","import os\n","\n","from keras.layers import *\n","from keras.models import Model\n","from keras import backend as K\n","from keras import Sequential\n","from keras.callbacks import ModelCheckpoint\n","from keras.callbacks import EarlyStopping\n","from sklearn.metrics import confusion_matrix\n","from sklearn.preprocessing import scale\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Install mne library for topoplot\n","!pip install mne\n","import mne\n","\n","warnings.filterwarnings('ignore')\n","drive.mount('/content/drive')\n","\n","####################### SETTINGS ########################\n","# Set this variable with the desidered subject's letter #\n","SUBJECT_SELECTED = \"B\"                                  #\n","#########################################################\n","\n","subject_names = [\"A\", \"B\"]\n","\n","# Check for errors in the settings\n","if SUBJECT_SELECTED not in subject_names:\n","    raise ValueError(\"SUBJECT_SELECTED value {} is invalid.\\nPlease enter one of the following parameters {}\".format(SUBJECT_SELECTED, subject_names))\n","\n","# Google drive data paths\n","MODEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Trained_models/CNN2b/' + SUBJECT_SELECTED\n","SUBJECT_TRAIN_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Train.mat'\n","SUBJECT_TEST_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Test.mat'\n","CHANNEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/channels.csv'\n","CHANNEL_COORD = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/coordinates.csv'\n","\n","# 8 channels are obtained by selection of first layer in CNN1\n","if SUBJECT_SELECTED == \"A\":\n","    CHANNELS = [10, 14, 17, 50, 55, 57, 59, 60]\n","elif SUBJECT_SELECTED == \"B\":\n","    CHANNELS = [17, 50, 55, 56, 57, 58, 59, 60]"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Collecting mne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a1/7c/ad1b52a3fdd4be8f55e183f1eff7d76f48cd1bee83c5630f9c26770e032e/mne-0.19.2-py3-none-any.whl (6.4MB)\n","\u001b[K     |████████████████████████████████| 6.4MB 30.2MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from mne) (1.17.4)\n","Requirement already satisfied: scipy>=0.17.1 in /usr/local/lib/python3.6/dist-packages (from mne) (1.3.3)\n","Installing collected packages: mne\n","Successfully installed mne-0.19.2\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/numba/decorators.py:146: RuntimeWarning: Caching is not available when the 'parallel' target is in use. Caching is now being disabled to allow execution to continue.\n","  warnings.warn(msg, RuntimeWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CVrJuHCMFCSl","colab_type":"text"},"source":["# Training set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signals from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, 8)**;\n","6.   Obtain **(noP300 / P300)** class ratio to balance out dataset during training;\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"78aU0w6-Lp0y","colab_type":"code","outputId":"4885f302-2b9b-4d2d-9090-1b2539f497ca","executionInfo":{"status":"ok","timestamp":1576425854379,"user_tz":-60,"elapsed":30419,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["if not os.path.exists(SUBJECT_TRAIN_FILE_PATH):\n","    print(\"Missing file: {}\".format(SUBJECT_TRAIN_FILE_PATH))\n","else:\n","    # Load the required data\n","    data = loadmat(SUBJECT_TRAIN_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals = data['Signal']\n","    # Take only the 8 selected channels of the signal\n","    signals = signals[:, :, CHANNELS]\n","    \n","    flashing = data['Flashing']\n","    stimulus = data['StimulusType']\n","    word = data['TargetChar']\n","    \n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION = (len(signals))*(len(signals[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS = len(word[0])\n","    # Set flag to True to balance the training set\n","    BALANCE_DATASET = False\n","\n","    print(\"**********************************\")\n","    print(\"       TRAIN SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION)\n","    print(\"Number of letters:  %d\" % TRIALS)\n","    print(\"Spelled word:       %s\" % ''.join(word))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","       TRAIN SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   46.01 min\n","Number of letters:  85\n","Spelled word:       VGREAAH8TVRHBYN_UGCOLO4EUERDOOHCIFOMDNU6LQCPKEIREKOYRQIDJXPBKOJDWZEUEWWFOEBHXTQTTZUMO\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IXuVC7puKcLt","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS):\n","    signals[trial, :, :] = signal.filtfilt(b, a, signals[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jS1FuKDuixnF","colab_type":"code","outputId":"8715e61f-23e5-44a8-a497-ebfcdf590034","executionInfo":{"status":"ok","timestamp":1576425855224,"user_tz":-60,"elapsed":31246,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals[0])))\n","\n","signals = signals[:, 0:-1:SCALE_FACTOR, :]\n","flashing = flashing[:, 0:-1:SCALE_FACTOR]\n","stimulus = stimulus[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bGaRKwu9MIvP","colab_type":"code","outputId":"12892424-3e8a-4175-f5a9-142e52a314a8","executionInfo":{"status":"ok","timestamp":1576425856238,"user_tz":-60,"elapsed":32246,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = len(CHANNELS)\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL = len(signals[0])\n","\n","train_features = []\n","train_labels = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS):\n","    for sample in (range(SAMPLES_PER_TRIAL)):\n","        if (sample == 0) or (flashing[trial, sample-1] == 0 and flashing[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals[trial, lower_sample:upper_sample, :]                \n","            # Features extraction\n","            train_features.append(window)\n","            # Labels extraction\n","            if stimulus[trial, sample] == 1:\n","                count_positive += 1\n","                train_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                train_labels.append(0) # Class no-P300\n","\n","# Get negative-positive classes ratio\n","train_ratio = count_negative/count_positive\n","\n","# Convert lists to numpy arrays\n","train_features = np.array(train_features)\n","train_labels = np.array(train_labels)\n","\n","# 3D Tensor shape (SAMPLES, 78, 8)\n","dim_train = train_features.shape\n","print(\"Features tensor shape: {}\".format(dim_train))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (15300, 78, 8)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kKMlV1Mqapew","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(train_features)):\n","    train_features[pattern] = scale(train_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xTVrfKzJH2aE","colab_type":"text"},"source":["# Testing set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signas from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, 8)**;\n","6.   Calculate weights vector to balance samples importance and obtain correct accuracy estimation;"]},{"cell_type":"code","metadata":{"id":"Nm-bZZsKTliz","colab_type":"code","outputId":"17503d40-d1ff-4c7d-a893-3bd59fb53bdd","executionInfo":{"status":"ok","timestamp":1576425864538,"user_tz":-60,"elapsed":40529,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["# Test data loading\n","if not os.path.exists(SUBJECT_TEST_FILE_PATH):\n","    print(\"Missing file: {}\", SUBJECT_TEST_FILE_PATH)\n","else:\n","    # Load the required data\n","    data_test = loadmat(SUBJECT_TEST_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals_test = data_test['Signal']\n","    signals_test = signals_test[:, :, CHANNELS]\n","    flashing_test = data_test['Flashing']\n","    word_test =  data_test['TargetChar']\n","    stimulus_code_test = data_test['StimulusCode']\n","\n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION_TEST = (len(signals_test))*(len(signals_test[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS_TEST = len(word_test[0])\n","    # Number of samples for each character in trials\n","    SAMPLES_PER_TRIAL_TEST = len(signals_test[0])\n","    \n","    print(\"**********************************\")\n","    print(\"        TEST SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION_TEST)\n","    print(\"Number of letters:  %d\" % TRIALS_TEST)\n","    print(\"Spelled word:       %s\" % ''.join(word_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","        TEST SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   54.12 min\n","Number of letters:  100\n","Spelled word:       MERMIROOMUHJPXJOHUVLEORZP3GLOO7AUFDKEFTWEOOALZOP9ROCGZET1Y19EWX65QUYU7NAK_4YCJDVDNGQXODBEV2B5EFDIDNR\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SvCresQJm25A","colab_type":"code","colab":{}},"source":["# Create characters matrix\n","char_matrix = [[0 for j in range(6)] for i in range(6)]\n","s = string.ascii_uppercase + '1' + '2' + '3' + '4' + '5' + '6' + '7' + '8' + '9' + '_'\n","\n","# Append cols and rows in a list\n","list_matrix = []\n","for i in range(6):\n","    col = [s[j] for j in range(i, 36, 6)]\n","    list_matrix.append(col)\n","for i in range(6):\n","    row = [s[j] for j in range(i * 6, i * 6 + 6)]\n","    list_matrix.append(row)\n","\n","# Create StimulusType array for the test set (missing from the given database)\n","stimulus_test = [[0 for j in range(SAMPLES_PER_TRIAL_TEST)] for i in range(TRIALS_TEST)]\n","stimulus_test = np.array(stimulus_test)\n","\n","for trial in range(TRIALS_TEST):\n","    counter=0\n","    for sample in range(SAMPLES_PER_TRIAL_TEST):\n","        index = int(stimulus_code_test[trial, sample]) - 1\n","        if not index == -1:\n","            if word_test[0][trial] in list_matrix[index]:\n","                stimulus_test[trial, sample] = 1\n","            else:\n","                stimulus_test[trial, sample] = 0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UNy5oI8ooMAw","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS_TEST):\n","    signals_test[trial, :, :] = signal.filtfilt(b, a, signals_test[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8CpKeth5qOjr","colab_type":"code","outputId":"e1959811-f82b-42a2-9d0f-15d18864dc37","executionInfo":{"status":"ok","timestamp":1576425865574,"user_tz":-60,"elapsed":41545,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals_test[0])))\n","\n","signals_test = signals_test[:, 0:-1:SCALE_FACTOR, :]\n","flashing_test = flashing_test[:, 0:-1:SCALE_FACTOR]\n","stimulus_test = stimulus_test[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals_test[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"48sta55OVXFy","colab_type":"code","outputId":"f7c4571b-71de-4a80-c98e-87e9e2fb751a","executionInfo":{"status":"ok","timestamp":1576425866871,"user_tz":-60,"elapsed":42830,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = len(CHANNELS)\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL_TEST = len(signals[0])\n","\n","test_features = []\n","test_labels = []\n","windowed_stimulus = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS_TEST):\n","    for sample in (range(SAMPLES_PER_TRIAL_TEST)):\n","        if (sample == 0) or (flashing_test[trial, sample-1] == 0 and flashing_test[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals_test[trial, lower_sample:upper_sample, :]\n","            # Extracting number of row/col in a window\n","            number_stimulus = int(stimulus_code_test[trial, sample])\n","            windowed_stimulus.append(number_stimulus)\n","            # Features extraction\n","            test_features.append(window)\n","            # Labels extraction\n","            if stimulus_test[trial, sample] == 1:\n","                count_positive += 1\n","                test_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                test_labels.append(0) # Class no-P300\n","\n","# Get test weights to take into account the number of classes \n","test_weights = []\n","for i in range(len(test_labels)):\n","    if test_labels[i] == 1:\n","        test_weights.append(len(test_labels)/count_positive)\n","    else:\n","        test_weights.append(len(test_labels)/count_negative)\n","test_weights = np.array(test_weights)\n","\n","# Convert lists to numpy arrays\n","test_features = np.array(test_features)\n","test_labels = np.array(test_labels)\n","\n","# 3D tensor (SAMPLES, 78, 64)\n","dim_test = test_features.shape\n","print(\"Features tensor shape: {}\".format(dim_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (18000, 78, 8)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-VY8Nr04YI9F","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(test_features)):\n","    test_features[pattern] = scale(test_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-VK4MFXTjHoM","colab_type":"text"},"source":["# CNN2b model definition, training and testing\n","\n","1.   Function definiton for randomization of weights and biases;\n","2.   **Scaled_tanh(x)** activation function definition;\n","3.   ANN model definition (2 Conv1D layers, 2 dense layers);\n","4.   Training of the network over weighted dataset;\n","5.   CNN2b performance assessment;"]},{"cell_type":"code","metadata":{"id":"eTIc_w_TcOmK","colab_type":"code","colab":{}},"source":["# Randomizing function for bias and weights of the network\n","def cecotti_normal(shape, dtype = None, partition_info = None):\n","    if len(shape) == 1:\n","        fan_in = shape[0]\n","    elif len(shape) == 2:\n","        fan_in = shape[0]\n","    else:\n","        receptive_field_size = 1\n","        for dim in shape[:-2]:\n","            receptive_field_size *= dim\n","        fan_in = shape[-2] * receptive_field_size\n","    return K.random_normal(shape, mean = 0.0, stddev = (1.0 / fan_in))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VeyyvqGgcTsa","colab_type":"code","colab":{}},"source":["# Custom tanh activation function\n","def scaled_tanh(z):\n","    return 1.7159 * K.tanh((2.0 / 3.0) * z)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BuMCOgJ3ST-h","colab_type":"code","colab":{}},"source":["# Build the model\n","def CNN2b_model(channels=8, filters=10):\n","    model = Sequential([\n","        Conv1D(\n","            filters = filters,\n","            kernel_size = 1,\n","            padding = \"same\",\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","            input_shape = (78, channels)\n","        ),\n","        Conv1D(\n","            filters = 50,\n","            kernel_size = 13,\n","            padding = \"valid\",\n","            strides = 11,\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","        ),\n","        Flatten(),\n","        Dense(100, activation=\"sigmoid\"),\n","        Dense(1, activation=\"sigmoid\")\n","    ])\n","    model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jVMdFs4quaZL","colab_type":"code","outputId":"a6081a47-5d7d-42c8-eafd-0325a54ea0e6","executionInfo":{"status":"ok","timestamp":1576425873834,"user_tz":-60,"elapsed":49760,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":505}},"source":["# Training parameters\n","BATCH_SIZE = 256\n","EPOCHS = 200\n","VALID_SPLIT = 0.05\n","SHUFFLE = 1 # set to 1 to shuffle subsets during training\n","\n","# Model summary\n","CNN2b_model(channels=8, filters=10).summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv1d_1 (Conv1D)            (None, 78, 10)            90        \n","_________________________________________________________________\n","conv1d_2 (Conv1D)            (None, 6, 50)             6550      \n","_________________________________________________________________\n","flatten_1 (Flatten)          (None, 300)               0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 100)               30100     \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 101       \n","=================================================================\n","Total params: 36,841\n","Trainable params: 36,841\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GEPpgyxyTl-u","colab_type":"code","outputId":"9488b2df-73f8-476b-a234-a52c2ed8827a","executionInfo":{"status":"ok","timestamp":1576425908096,"user_tz":-60,"elapsed":84004,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Model definition\n","model = CNN2b_model(channels=8, filters=10)\n","\n","# Callback to save best model only\n","checkpoint = ModelCheckpoint(filepath=MODEL_LOCATIONS_FILE_PATH + \"/\" + \"model\" + \".h5\", \n","                             monitor='val_loss', \n","                             mode='min',\n","                             save_best_only=True)\n","\n","# Callback to stop when loss on validation set doesn't decrease in 50 epochs\n","earlystop = EarlyStopping(monitor = 'val_loss', \n","                              mode = 'min', \n","                              patience = 50, \n","                              restore_best_weights = True)\n","\n","# Callback to keep track of model statistics\n","history = model.fit(x=train_features, \n","                    y=train_labels, \n","                    batch_size=BATCH_SIZE, \n","                    epochs=EPOCHS, \n","                    validation_split=VALID_SPLIT, \n","                    callbacks=[checkpoint, earlystop],\n","                    shuffle=SHUFFLE,\n","                    class_weight={0: 1., 1: train_ratio})"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Train on 14535 samples, validate on 765 samples\n","Epoch 1/200\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","14535/14535 [==============================] - 7s 496us/step - loss: 0.3694 - acc: 0.5968 - val_loss: 0.3120 - val_acc: 0.7346\n","Epoch 2/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.3181 - acc: 0.7202 - val_loss: 0.2753 - val_acc: 0.7320\n","Epoch 3/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.3029 - acc: 0.7407 - val_loss: 0.2750 - val_acc: 0.7255\n","Epoch 4/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2968 - acc: 0.7460 - val_loss: 0.2632 - val_acc: 0.7569\n","Epoch 5/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2955 - acc: 0.7461 - val_loss: 0.2634 - val_acc: 0.7333\n","Epoch 6/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2940 - acc: 0.7492 - val_loss: 0.2543 - val_acc: 0.7843\n","Epoch 7/200\n","14535/14535 [==============================] - 0s 22us/step - loss: 0.2914 - acc: 0.7534 - val_loss: 0.2506 - val_acc: 0.8052\n","Epoch 8/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2906 - acc: 0.7558 - val_loss: 0.2535 - val_acc: 0.7386\n","Epoch 9/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2916 - acc: 0.7513 - val_loss: 0.2531 - val_acc: 0.7699\n","Epoch 10/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2907 - acc: 0.7490 - val_loss: 0.2588 - val_acc: 0.7386\n","Epoch 11/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2874 - acc: 0.7564 - val_loss: 0.2474 - val_acc: 0.7830\n","Epoch 12/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2875 - acc: 0.7568 - val_loss: 0.2527 - val_acc: 0.7699\n","Epoch 13/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2874 - acc: 0.7545 - val_loss: 0.2520 - val_acc: 0.7699\n","Epoch 14/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2859 - acc: 0.7579 - val_loss: 0.2515 - val_acc: 0.7595\n","Epoch 15/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2856 - acc: 0.7591 - val_loss: 0.2533 - val_acc: 0.7451\n","Epoch 16/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2851 - acc: 0.7551 - val_loss: 0.2519 - val_acc: 0.7660\n","Epoch 17/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2851 - acc: 0.7542 - val_loss: 0.2508 - val_acc: 0.7660\n","Epoch 18/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2846 - acc: 0.7576 - val_loss: 0.2475 - val_acc: 0.7608\n","Epoch 19/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2827 - acc: 0.7561 - val_loss: 0.2490 - val_acc: 0.7804\n","Epoch 20/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2828 - acc: 0.7598 - val_loss: 0.2452 - val_acc: 0.7739\n","Epoch 21/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2831 - acc: 0.7648 - val_loss: 0.2496 - val_acc: 0.7412\n","Epoch 22/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2828 - acc: 0.7568 - val_loss: 0.2472 - val_acc: 0.7673\n","Epoch 23/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2800 - acc: 0.7588 - val_loss: 0.2477 - val_acc: 0.7791\n","Epoch 24/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2801 - acc: 0.7605 - val_loss: 0.2472 - val_acc: 0.7412\n","Epoch 25/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2796 - acc: 0.7619 - val_loss: 0.2411 - val_acc: 0.7869\n","Epoch 26/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2778 - acc: 0.7636 - val_loss: 0.2414 - val_acc: 0.7582\n","Epoch 27/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2775 - acc: 0.7639 - val_loss: 0.2486 - val_acc: 0.7333\n","Epoch 28/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2765 - acc: 0.7642 - val_loss: 0.2435 - val_acc: 0.7765\n","Epoch 29/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2746 - acc: 0.7659 - val_loss: 0.2415 - val_acc: 0.7712\n","Epoch 30/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2736 - acc: 0.7675 - val_loss: 0.2384 - val_acc: 0.7739\n","Epoch 31/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2731 - acc: 0.7673 - val_loss: 0.2390 - val_acc: 0.8013\n","Epoch 32/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2723 - acc: 0.7663 - val_loss: 0.2386 - val_acc: 0.7922\n","Epoch 33/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2712 - acc: 0.7725 - val_loss: 0.2457 - val_acc: 0.7725\n","Epoch 34/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2697 - acc: 0.7688 - val_loss: 0.2391 - val_acc: 0.7725\n","Epoch 35/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2674 - acc: 0.7748 - val_loss: 0.2390 - val_acc: 0.7660\n","Epoch 36/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2670 - acc: 0.7726 - val_loss: 0.2413 - val_acc: 0.7595\n","Epoch 37/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2647 - acc: 0.7747 - val_loss: 0.2410 - val_acc: 0.7634\n","Epoch 38/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2635 - acc: 0.7766 - val_loss: 0.2377 - val_acc: 0.7686\n","Epoch 39/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2625 - acc: 0.7740 - val_loss: 0.2432 - val_acc: 0.7556\n","Epoch 40/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2605 - acc: 0.7809 - val_loss: 0.2391 - val_acc: 0.7647\n","Epoch 41/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2593 - acc: 0.7794 - val_loss: 0.2376 - val_acc: 0.7882\n","Epoch 42/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2568 - acc: 0.7840 - val_loss: 0.2410 - val_acc: 0.7634\n","Epoch 43/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2548 - acc: 0.7830 - val_loss: 0.2412 - val_acc: 0.7647\n","Epoch 44/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2530 - acc: 0.7858 - val_loss: 0.2400 - val_acc: 0.7778\n","Epoch 45/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2500 - acc: 0.7886 - val_loss: 0.2396 - val_acc: 0.7660\n","Epoch 46/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2489 - acc: 0.7915 - val_loss: 0.2399 - val_acc: 0.7569\n","Epoch 47/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2452 - acc: 0.7908 - val_loss: 0.2424 - val_acc: 0.7634\n","Epoch 48/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2440 - acc: 0.7953 - val_loss: 0.2452 - val_acc: 0.7673\n","Epoch 49/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2402 - acc: 0.7950 - val_loss: 0.2446 - val_acc: 0.7621\n","Epoch 50/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2364 - acc: 0.8020 - val_loss: 0.2429 - val_acc: 0.7647\n","Epoch 51/200\n","14535/14535 [==============================] - 0s 22us/step - loss: 0.2337 - acc: 0.8055 - val_loss: 0.2445 - val_acc: 0.7830\n","Epoch 52/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2296 - acc: 0.8080 - val_loss: 0.2411 - val_acc: 0.8000\n","Epoch 53/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2258 - acc: 0.8140 - val_loss: 0.2434 - val_acc: 0.7673\n","Epoch 54/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2210 - acc: 0.8180 - val_loss: 0.2475 - val_acc: 0.7817\n","Epoch 55/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2171 - acc: 0.8211 - val_loss: 0.2455 - val_acc: 0.7712\n","Epoch 56/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2139 - acc: 0.8230 - val_loss: 0.2473 - val_acc: 0.7660\n","Epoch 57/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2106 - acc: 0.8256 - val_loss: 0.2477 - val_acc: 0.7830\n","Epoch 58/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2051 - acc: 0.8321 - val_loss: 0.2489 - val_acc: 0.7843\n","Epoch 59/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2010 - acc: 0.8350 - val_loss: 0.2497 - val_acc: 0.7778\n","Epoch 60/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1965 - acc: 0.8427 - val_loss: 0.2492 - val_acc: 0.7725\n","Epoch 61/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1931 - acc: 0.8444 - val_loss: 0.2536 - val_acc: 0.7647\n","Epoch 62/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1888 - acc: 0.8440 - val_loss: 0.2520 - val_acc: 0.7817\n","Epoch 63/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.1835 - acc: 0.8539 - val_loss: 0.2559 - val_acc: 0.7935\n","Epoch 64/200\n","14535/14535 [==============================] - 0s 22us/step - loss: 0.1783 - acc: 0.8590 - val_loss: 0.2519 - val_acc: 0.7869\n","Epoch 65/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1737 - acc: 0.8630 - val_loss: 0.2528 - val_acc: 0.7908\n","Epoch 66/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.1709 - acc: 0.8680 - val_loss: 0.2497 - val_acc: 0.7673\n","Epoch 67/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.1690 - acc: 0.8650 - val_loss: 0.2555 - val_acc: 0.7987\n","Epoch 68/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1612 - acc: 0.8743 - val_loss: 0.2553 - val_acc: 0.7987\n","Epoch 69/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1564 - acc: 0.8798 - val_loss: 0.2543 - val_acc: 0.7987\n","Epoch 70/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1527 - acc: 0.8834 - val_loss: 0.2540 - val_acc: 0.7974\n","Epoch 71/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1478 - acc: 0.8870 - val_loss: 0.2564 - val_acc: 0.8052\n","Epoch 72/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.1452 - acc: 0.8890 - val_loss: 0.2563 - val_acc: 0.7869\n","Epoch 73/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.1405 - acc: 0.8938 - val_loss: 0.2601 - val_acc: 0.8013\n","Epoch 74/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.1355 - acc: 0.8984 - val_loss: 0.2642 - val_acc: 0.8078\n","Epoch 75/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1311 - acc: 0.9035 - val_loss: 0.2676 - val_acc: 0.8013\n","Epoch 76/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1268 - acc: 0.9080 - val_loss: 0.2607 - val_acc: 0.8105\n","Epoch 77/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1236 - acc: 0.9080 - val_loss: 0.2616 - val_acc: 0.8052\n","Epoch 78/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1199 - acc: 0.9112 - val_loss: 0.2658 - val_acc: 0.8039\n","Epoch 79/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.1156 - acc: 0.9167 - val_loss: 0.2655 - val_acc: 0.8013\n","Epoch 80/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.1120 - acc: 0.9183 - val_loss: 0.2677 - val_acc: 0.8131\n","Epoch 81/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.1086 - acc: 0.9200 - val_loss: 0.2705 - val_acc: 0.8026\n","Epoch 82/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1057 - acc: 0.9257 - val_loss: 0.2668 - val_acc: 0.7922\n","Epoch 83/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1022 - acc: 0.9267 - val_loss: 0.2778 - val_acc: 0.8170\n","Epoch 84/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0988 - acc: 0.9303 - val_loss: 0.2783 - val_acc: 0.8222\n","Epoch 85/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.0959 - acc: 0.9314 - val_loss: 0.2768 - val_acc: 0.8105\n","Epoch 86/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0924 - acc: 0.9343 - val_loss: 0.2795 - val_acc: 0.8118\n","Epoch 87/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.0898 - acc: 0.9377 - val_loss: 0.2835 - val_acc: 0.8170\n","Epoch 88/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.0870 - acc: 0.9382 - val_loss: 0.2801 - val_acc: 0.8065\n","Epoch 89/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.0837 - acc: 0.9417 - val_loss: 0.2832 - val_acc: 0.8118\n","Epoch 90/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.0809 - acc: 0.9442 - val_loss: 0.2945 - val_acc: 0.8275\n","Epoch 91/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.0782 - acc: 0.9459 - val_loss: 0.2898 - val_acc: 0.8170\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eJHaj2f1VFYZ","colab_type":"code","outputId":"2086134d-f022-4636-a718-412ac784c8f6","executionInfo":{"status":"ok","timestamp":1576425908390,"user_tz":-60,"elapsed":84283,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["plt.figure(figsize=(15,4))\n","plt.style.use('tableau-colorblind10')\n","\n","# Plots of loss curves during training\n","plt.subplot(1,2,1)\n","plt.plot(history.history['loss'], label=\"training loss\")\n","plt.plot(history.history['val_loss'], label=\"validation loss\")\n","plt.legend()\n","\n","# Plots of accuracy curves during training\n","plt.subplot(1,2,2)\n","plt.plot(history.history['acc'], label=\"training accuracy\")\n","plt.plot(history.history['val_acc'], label=\"validation accuracy\")\n","plt.legend()\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3AAAAD4CAYAAACt4QT/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndCg0K1AptRFIA0ZprZcVse4wYBhAnTp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sY7lVALaXwOVkwl/vQL22UK89ALeH1MTHzYlJ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\nucTkFBKSU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WOCgHBgD3qdSd7QNTIHdI3MPV0fc0fXx/Om66NHQUvgKgO7vO7HerZJNowx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\nIn7i/wE8yTaFInHQiAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Sg1zrX0xZTRt","colab_type":"code","colab":{}},"source":["# Define model\n","best_model = CNN2b_model(channels=8, filters=10)\n","\n","# Load best model weights from h5 file\n","best_model.load_weights(MODEL_LOCATIONS_FILE_PATH + \"/model.h5\")\n","\n","# Compile best model model\n","best_model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_j_iqu0tYlbS","colab_type":"code","outputId":"f773c762-6114-4a24-c46c-e67b0a4f7fa9","executionInfo":{"status":"ok","timestamp":1576425911329,"user_tz":-60,"elapsed":87206,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":377}},"source":["# Load topoplot coordinates\n","if not os.path.exists(CHANNEL_COORD):\n","    print(\"Missing file: {}\".format(CHANNEL_COORD))\n","else:\n","    xycoord = []\n","    # Read file content\n","    with open(CHANNEL_COORD, \"r\") as f:\n","        file_content = f.read()\n","        # Loop over all rows\n","        for row in file_content.split(\"\\n\"): \n","        # Skip missing rows\n","            if row == '':\n","                continue\n","            xycoord.append(row)\n","\n","# Create a list with x,y coordinates of each electrodes well formatted\n","coord = []\n","for x in xycoord:\n","    coord.append(x.split(','))\n","coord = np.array(coord)\n","\n","# Flip y axis in order to plot in the right way the electrodes positions\n","for i in range(len(coord)):\n","    coord[i][1] = 681 -int(coord[i][1])\n","\n","# Create a list of weights for all filters\n","nf = []\n","for filt in range(10):\n","    nf.append(abs(np.array(best_model.get_weights()[0])[0, :, filt]))\n","nf = np.array(nf, dtype=float)\n","\n","# Add -1 to missing channels values\n","tmp_full = []\n","for i in range(10):\n","    tmp = np.full(64, 0, dtype=float)\n","    for c in range(N_CHANNELS):\n","        tmp[CHANNELS[c]] = nf[i][c]\n","    tmp_full.append(tmp)\n","nf = np.array(tmp_full, dtype=float)\n","\n","# Plot topoplot of the 10 filters in the first convolutional layer\n","fig = plt.figure(figsize=(15,6))\n","for i in range(10):\n","    ax = fig.add_subplot(2,5,i+1)\n","    ax.set_title(\"Filter #{}\".format(i+1))\n","    mne.viz.plot_topomap(nf[i], coord, cmap='Spectral_r', axes=ax, show=False)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1MAAAFoCAYAAACymqHbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd3hc5ZU/8O+5904f9V6t5iK52xhs\nsDEGjMG0UAKEbDZ1CambkEKyqZuy6ckmm0J2k9/CJgEChN4NxgWMccVdtmSr9zqaftv5/TEjI9uS\nrD4a+f08jx8kzZ0754pX977nrcTMEARBEARBEARBEEZHinUAgiAIgiAIgiAI8UgkU4IgCIIgCIIg\nCGMgkilBEARBEARBEIQxEMmUIAiCIAiCIAjCGIhkShAEQRAEQRAEYQxEMiUIgiAIgiAIgjAGF1wy\nRUSFROQjIjn6/RYi+kSs4xKEoYgyK8QjUW6FeCPKrBBvRJmdHmZsMkVEtUQUjBay/n+5zFzPzG5m\nNgZ5z0eI6M1JjGkjET0c/fr/iOimAa/lENGzRNRMRExERZMVhzA9xWGZvZ6I3iSiXiJqJaI/EVHC\nZMUiTE9xWG7XEdGhaLntIqKniChvsmIRpp94K7NnHff/onWEssmKRZh+4q3MEtEVRGSeFe+HJyuW\nWJuxyVTUjdFC1v+veTI/jIiU8xyyHMCeAV/vG/CaCeBlALdNQmhC/IinMpsE4AcAcgGUA8gD8LOJ\njlGIC/FUbo8C2MDMyYiU3SoAf5jwIIXpLp7KbP85VgMoneDQhPgRb2W2+ax4H5rwIKeJmZ5MnYOI\niqKtOspZPy8H8ACAVdEMujf6cxsR/ZyI6omojYgeICJH9LUriKiRiO4nolYA/3uej78IwF4icgFI\nZebG/heYuY2Zfw9g9wRerjADTOMy+zAzv8zMAWbuAfA/AC6buCsX4tk0LrdtZ1VCDACilV+YtmU2\nej4FwH8B+NzEXK0wE0znMnshueCSqaEw8zEA9wJ4O5pBJ0df+jGAOQCWIPLAzQPw7QFvzQaQCmAW\ngHsGOzcRHY8W5BsAPAugDUA6RYaZ/HEyrkeY+aZhmb0cwJHxXZUw002HckuReQa9AIIAvgzgpxN4\nicIMMx3KLIAvAtjGzAcn7sqEmWqalNnMaMJWQ0S/iiZdM9JMT6aejv7P7SWip0f7ZiIiRArTF5m5\nm5m9AP4DwF0DDjMBfIeZw8wcHOw8zDwXwO0AnmXmJAAPA7ibmZOZ+ZOjjUuY0eKyzBLRegAfxpk3\nZeHCEVflNjrPIBlAOoBvAqgcbcxC3IubMktEBQA+CXF/vdDFTZlF5J66BEAOgCsRGQb4y9HGHC/O\nNx4y3r2PmV8bx/szADgR6cbs/xkBkAcc08HMoaFOQEQ/RaTwOgDo0Ww+AcAdRPRfzJw9jviEmSfu\nyiwRrUTkZno7M58YR+xC/Iq7cgsAzNxNRA8BOEBEecysj+MahPgST2X2PwF8j5k944hXiH9xU2aZ\nuRVAa/RtNUT0VQDPI9IoMOPM9J6p0eKzvu9EZBjI/GjGnczMSczsHuY9Z56Q+avRFtAaRLpU1yLS\n7ZosEilhAsS0zBLRUkS6+T/GzK+P92KEC8Z0utcqADIBJI76KoQLSSzL7FUAfkaRVVP7K6hvE9Hd\n47oiYaabTvdZxgzOOWbshY1RG4B8IrICADObiEyq/xURZQIAEeUR0YbRnJQiy0UnMHMLgGV4b/WT\ns4+zA7BFv7VFvxeE4cSszBLRAkRWoPwcMz83vssQLjCxLLe3EtFcIpKIKAORoSf7mbl7fJckzHCx\nrB/MAbAYkWFTS6I/uxHAU2O5EOGCEcv77DoimkURBYjM1XpmfJczfYlk6kybEZlA30pEndGf3Q+g\nGsBOIuoD8BqAuaM871IA70a/XgZg7xDHBQH4ol9XRr8XhOHEssx+CZFhA3+m9/aREAtQCCMRy3Kb\nh0gjgBfAIUTmCNwyys8RLjwxK7PM3M7Mrf3/oj/uHGpOiyBExfI+uxTADgD+6H8PAfj8KD8nbhDz\nsD16giAIgiAIgiAIwiBEz5QgCIIgCIIgCMIYiGRKEARBEARBEARhDEQyJQiCIAiCIAiCMAYimRIE\nQRAEQRAEQRgDkUwJgiAIgiAIgiCMgUimBEEQBEEQBEEQxkAkU4IgCIIgCIIgCGMgkilBEARBEARB\nEIQxEMmUIAiCIAiCIAjCGIhkShAEQRAEQRAEYQxEMjUORESxjkEQRkOUWSHeiDIrxBtRZoV4JMrt\n2IlkaoyI6PMA6ono4ljHIggjQUTXA2gmovfHOhZBGAkiWgzgJBH9m3jQC/GAiHIA7CKi/0dE1ljH\nIwjnQ0ROInoSwOtElBLreOKRSKZGiYgkIvolgHsB/DuA54nophiHJQjDIqJ7APwJwNcB/JKIviQq\np8J0RkTrAWwC8FMA7wfwABEpsY1KEIZGRBUA3gbwHIB0AC8QUWJsoxKEoRFRJoA3APQBOADgLSKa\nFduo4o9IpkaBiBwAHgOwHMBlzPwnABsRech/JqbBCcIgosn/fwD4MoDVzPwggEsBfBTAr4lIjmV8\ngjAYIvoIgL8AuJWZHwBwOYBZAJ4hIncsYxOEwRDRFYhUSr/JzN8DcAuAKgDbiSg/lrEJwmCIaA4i\nyf/LAD7KzF8E8EcAO4hoWUyDizMimRohIkoD8BoADcA1zNwDAMy8B8BlAD5HRD8lIvE7FaYFIrIh\nUiG9AsClzHwSAJi5AcBqAAsAPEFEzpgFKQgDUMR3AHwbwBXM/CYAMLMXwI0AWgFsJaLsGIYpCGcg\nog8g0tD6AWb+KwAwswHgMwD+hkjldGEMQxSEMxDRpQC2AfgPZv4OMzMAMPOvAXwewCtEdF0sY4wn\nouI/AkRUAmAHgO0APsjM4YGvM3MNIgnVKgAPE5F96qMUhPcQUTIirU0OAFcxc+fA15m5F8C1AHyI\njJPOmPooBeE9RGRBZCjqjYgk/5UDX2dmDcAnADwN4G0iKp/6KAXhPdHk/34APwZwJTNvHvg6R/wU\nwP2I3GevikWcgjAQEd2GyH30I8z857NfZ+Z/ALgZwP8S0SemOr54JJKp84guMPEmgF8z89eY2Rzs\nOGbuArAekd/pK0SUOoVhCsJpRFQI4C1Exj+/n5mDgx3HzCqAfwawGZGW09lTF6UgvCc6r+R5AFmI\n9Ei1DnZctHL6fQDfBbCFiC6fuigF4T3R+Xu/A3A3Isn/4aGOZeZHEJn39zAR/fMUhSgI5yCiLwD4\nNYANzPzyUMcx8w5Ehld/jYi+L+ZYD08kU8MgohsBvADgXmb+/fmOZ+YQgLsA7EZkEl/RpAYoCGch\noiWI9KL+iZm/EB1qMqRo5fQbAH4GYBsRrZyKOAWhHxHlIjLcpAbA+5jZd773MPNDAP4JkWGqd05y\niIJwBiJyAXgKQBmANczcdL73MPNWAOsAfI+Ivikqp8JUis6f/hWAexCZ87//fO9h5hOIzLG+BsBD\nYnXKoYlkaghE9CkA/w3gemZ+dqTvY2aTmb8M4PeIJFTLJytGQRiIiDYAeBXAF5j5V6N5LzP/N4CP\nA3iOiG6ZjPgE4WxENB+RCdB/B/ApZtZH+l5m3oTIaICfE9GXReVUmApElIXIQhOdiNQP+kb6XmY+\nish0gFsB/Hd0aKsgTKoBi6ctRSSRqhvpe5m5HZFGgCQALxJR0uREGd9EMnWWaPb+EwBfRGT1s11D\nHEfDrYTGzP8F4LMAXiaijZMTrSBEENHHATyEyOpnTwxxDBGRMlSlk5lfBHAdgN8S0ecmL1pBAIho\nHSKV0n9j5h/1T4Ae5Dh5mDJ7AJGW0w8D+C+xOqUwmYhoLiLJ/4sAPhadxzfYccOV2RYAawHkA3iW\niBImK15BIKJ0RBZPUxEZ2tczxHHSUAuoMXMAkQaASgBvElHBZMUbr2iI59cFKdpK9DcAsxHpWcpT\nFPtsieQCgHNM00hjNu0mmwqzEW1RIpZI0ogkjSQ5IJHcDqDZMLV6w1CPAzARmXz6DWb+n9hcmTCT\nEdH3AHwEwE8AJCmybY4kKYXRMpvBbDqYTYvJxuk9eiSSdYqU25AkKR0AWkxTr9eNcBWALkSWUn8W\nwH1DVXIFYayI6G4AvwHwcwCaLFnmyrJ1FoBck41MNk0Xs2Ex2bQATJH3SLpEskYkhSVJ7gKo1WSj\nXtfD1QDXITJ8pQPDzBMUhLEiolUAnkGk0apOkpS5imwtBiiP2cw02UhkNi1sGgqD5ch7JD36T5VI\n7iGSWpnNRt0In2Q2qxBZbKUIkRWC22J2ccKMRETFiOzVtxPANiJpriLbiomkfGYzy2Qjmdm0MZsK\nsxkpsyCDJLm/zPYRSW3M3GQY4VMmG5UAFgO4CcC1zHwodlc3vVzQyVS05WgegEsVxX4lmNfohprn\ntKf6U5MK5OTEAnuCK0Ny2JJgtyXBYU+CItshyxbIkgIiCaZpwDA1GIYGTQ8gGPIgGPYgGOpFn79V\n7/bUh3r6GiVNC1itFscxw9A2G6a2HcBbQ02yFoThRBeYWC3L1rUSyVdoeqjUbk0IpSQVICWx0Jbg\nylQc9mT0l1uLxQ5ZskKWFEiSDJNNmIYGw9Sg6aFome1FMOSBL9BudHvqQ719DWYw3Oe0KPZaZnOr\nboS3IFJmT8X48oU4FF0t8jJJUtYosvVKVQvOtyh2PSWxwEhJKrQlurItTkdy5D5rS4LV4oreZy2Q\nJBnMJgxTh2Fo0I0wQuE+BEO9CIY98AU6zN6+xlC3p97wB7tcsmxtkYje0vTQ64jMHzwiGgSE0Yr2\nGK0iki6zKParNT28VCJJSk7MV1MSC21JCblWpz0Z/ffaSJm1QpYtkEgGwDBNHYapQddVhFXv6bqB\nP9jNvX2N4W5PveoNtDmJZI8sWd7R9MAmRHq+9o5myKsgAKe3Q1kBYJXV4lyv6+rFDHYluXOCKUkF\nluSEfJvTkUoOWxIc9mTYrG4osjV6n420tZpswDA0GIaKsOqLlNlwLwLBHni8TeHuvvqwp6/ZzoBq\nUax7NT20idncAeCdaA/WBemCS6aiE0evUmTbLQBuVGSrIyt9HrLSyp0ZqWVIScw/XagmkqYF0dlb\ng47uKrO186i/o+ekVZKUekMPP2ay8TyAXUOtFChc2KI9pqslyXKTLCm3AZyRkTpHz04vd2WkllFa\ncjEUeeLnhRqGhm5PHTp6qtHaeczf1nVcApu9pmk8aZjaMwC2nb1NgCAAkSEjAJYRSTdaFPsdhqEV\np6WUqDnpFa6M1NlSekoJrJaJ397MNA30epvQ0V2Ntq7KYFtnpanqAY0gvaAboScBbIruWSUI54gO\n47veanHdqRvhJckJ+eGcjApHRupsJSOlDA77xE8XYWZ4/W2RMtt9PNzWcUz1h3pkWVI2a3rwcQAv\nR+etCMI5iCgPwEarxXmHbqir3c4MLSe9wpaZNseakVoGlyMdEz2dlJkRDPWgo6ca7V1Vekvn0WCf\nr8WqyLbdquZ/FMCL0S2DLhgXRDIVXcL0aqvFda9uhDekJBaGivIuTszPWiIlunMmvKCNhGnq6Oiu\nRkPr/nBd865wWPXpRPQXTQ/9WXSdCtFe00usFuc9hqHd7nKmG0V5l7jys5ZY0pKLEIu9oZkZ3Z46\nNLW9q9c27fJ5/W0WWbY+rWr+PyLSayUaAy5wRDRPUewfB/OHrRanrTB3ha0ge5ktM20O5ElopBqJ\nPl8rGtsOcF3zO56u3lq7RbG/EVZ9vwfwylBzXoQLBxHlyZLlQ5JsuYeArILsZSjIWe7MyZgPixKb\nLSMDoV40tR1AXfNuT1vnMZui2N4Nq/7fAfz0SFa7FGY2Ikomkt5vURyfNkxtXm7GQm1W7oqE3MyF\nsNtiMwVP1fxobj+C+pY9vqa2d2UiuVY3wg+Ypv4IM3fEJKgpNKOTKSLKk0j+HJF0b4I7S54za527\nKO8S2G2JsQ7tHD19jTjV8JZRXb81bJh6s66Hfgrgr2Ls/4UlstkufVyRrfdZLa6kOUXrnMX5qyjB\nlRnr0M7hC3Sitmknn6h9wx8K9wUMU/tPZvO/o3uuCReI6NCSuxTFfj+zWVpasFqaU3SlkpJYEJOG\nquGEVR/qmnfhRO0Wn8fbxAz+s2nq/zma1a2E+BftOb3OotjvN9m8uCj3YpQWrrFlpc2NSUPVcHQ9\njIa2/aiq3RJo7z4hEUlPGIb68+jiK8IFor+BVZFtXzXZ2JibscAsm7XWkZe5CLI8vRaFNE0DLR1H\nUF2/LdTYup+I5K26EfoJgDdm6pDrGZlMEdEKi+L8lsn6+pL8y7iidIMjKSE31mGNCLOJlo6jOFz1\nfF9Hd5UEoj8ahvqL6ApAwgxFRKWKYv86m8bduVmLjAVlG93pKWXTrjI6mEiPVS2OVL/ob2jZJ0uS\n/Limh37EzMdiHZsweYgoQ5YsXwDRZ9OSZmHB7BsT01NLcaDyKVSUXovp2AAwUGtnJfYc+pvu8bfo\nEilbNT3wfWZ+K9ZxCZOHiFxE0idk2Xq/057iWjj7xoSivItJnoRh0pMhGO5DVe0b+tGTL6sAV6pa\n4PsAnhWjAmau6MiqOyyK41uybC1YUHa9vbRwjWyzumId2ohoeginGnaYh6ue86taoEvTgz8E8JeZ\nNkVgRiVTRLTMojh+IUnyJQtn32Qvm3U5Tca4/KnS52vDsZMvq9X1200i+pNuhL8vxk7PLERUZFHs\nP2TmW+eVrFfmFV+tOB2psQ5rzIIhD07UbjaOnnxJA+glTQ/ez8xVsY5LmDhElCbL1q+D8eni/JVS\nRdl1tuSEvNOvG6aOvYcfwaK5N0/LUQBAJJFqbj+IxXNvgckGTtZv54Mnng0ahnZA0wP3MfPOWMco\nTBwichDJn5Ik+dvZ6RWWhbNvcGakzo6LxqrBmKaOuuY9OHj8aX8g1NOq6cH7ADw3U1v9L0TRbR7u\nVBT7z5Lc2UmL5rzPlZe9BNI06zkdKWbGgeNPoaZxhxoMeby6Ef46gAdnylDrGZFMEVGZItt+I0ny\n2sXzbrPPmbVWipeWppEIBHvwzsH/U5vaD5hg/Npk/Qdi3HR8I6J0WbL+CIQPVpRsUCrKNlripaVp\nJFQtiN2H/mrUNO00iOhxw1C/InpX41u0Qnq/RNKXi/JXSovn3uJwO9MHPVbVgth39O9YVvF+WC3T\np1ybpoHKmk1QZBtmz7rijMq0Yeo4Wb+d9x97PGAY2i7dCH82usmqEKeiFdKPybL1x1lpc63LKu50\npyYVxjqsCcPMaGjdh71HHvEFQ54m3Qh/kpm3xjouYXyI6HpFtv02wZWZdtGCuxOy0yviNvEHAN1Q\ncaT6RSS6s1GctxLt3VXYe+RRX7enzmcY6n0AHo33hoC4TqaIyGVRHN9jNj+1cM5NlvLSDcpkrGo2\nGFULIBDqwcAW2clS37IX/kAnCnKWY++RR/1NbQdU09Q/a7LxSLwXwAsNESmyZPkMiH5QVrBGWVx+\nq91unZoJo4apo7evEWnJRZP+OUeqXkSCKwO5mQtx6MRz4eO1rxsE+r5uhH8xU1qiLhSRsfr0PkW2\n/TE7o9y5YsEHXSMZwhdWfdh/7Aksn3/XmCfyMzO6ek8hLbl43HNZgqFeHDzxLOYUXYmUxPwhjzMM\nDcdrXzfePfYPlUh+SNMDX2Nmz7g+XJhyRLTKojgfTHBl5q5c/BF3ekrJlH12t6ceie7sSVll9Wwm\nmzh4/BmYps5VdVuCzOYbqua/l5kbJ/3DhQlFRLOtFuefFdm+bOXij7jyshZPWRLV52uF1eKa8AUs\nvP4OHK1+EfNnX4+zG99aOyux88D/84XCfVWqFvgIMx+c0A+fQnGbTBHRDbJs/d/8rCUJKxZ80OZ0\npEzo+XU9jD5/G3yBDgRDvQipXgwcltzWWQl/sBslBZcOHSMIblcmMlJKkODKHvUfBTOjsmYTLIoD\nZYVrTv+8vesEdrz750Ag1HNE10N3M3P16K9QmGpEtFyR7Y+kJObnrVzyMedwFbrRYmb4g13weJvh\nD3YhFPbAPGsYvcfbhNbOSpQVXj7khFWnPRmJ7lykJc2CxeIYdRxN7QfR3HYIc4uvRqI76/TP+3wt\n2HngwWBnz6l23Qjfzcw7Rn1yYcoR0SxFsf/Fbk1YvmrJx505GRWjen8w3IcDlU+itGA1MlLLhj1W\n00Po9tSht68RwVAvQIRw2Iva5l0oyF6K/uGvimxDgisTSQm5SHRlnXcrC91QUXlqE0xTQ3npdbAo\nthHHvvfII2pd8+6AYaj3MPPjI7tqIZaIKFmRbb+VJOXWixd+yFGcv2pCK6TMjFC4D15/G3zBToRC\nHqh6cMDrJqrrtiI1uXjohitmWCwOpCQWIiO1bMyNDYFgNw5VPY+5RVciOTEfuh7GoapnjaMnXwmb\npvFdZuOXzGyM6eTClCEiqyQp35ZIvm/R3PfZyks3SBO9+mlI9aLP13q6TqtpQWDA30Vd0y5YLS7k\nZM4fJD4JbkcaEt05SEksgDKCe6huqDh28hUQSSgvWY+hRouZbKKqdgv2Hn00xGz+2TDUr8bjflVx\nl0wRUZIi2/6gKLabLr/oM67s9PIJOW/lqU0IhT3o/20oshWJ7hy4nRlw2pNhsyZAkuTTx2t6GGHV\ne06mPRCzeXr/iJ6+RuhGCLkZC1GYe9F542E2ceD408jLXDRoJcRkE5WnXtX3H3sibJr615jN34tJ\nqNMTEVkkSfmOJClfXLnow47i/EtpIh7udc170N59ArJkARHB5UhDcmI+XI40OGyJ51QyTdOA19+O\npIScQc/HbCIY6kWvtxndnlqEVR9M04DblYnykvXDxhIM9+Fo9UtITynBrNwVQ5yfUd+yh3fs/1PQ\nZOOPhqH+GzOHxnb1wmSK9kZ9VJYsv1k49ybrgrKNlrHuv2eyidrGt9HtqYdFsSMY9qA4f1W0QtoO\nXY8UAUWxITVpFpITC+CwJZ2uAPd6m5Dkzj39vaYF4Q20o9fbBK+vDWa0rqjIVridGdD1MLo8tZiV\nuwId3VUwDA2zi9YNe68eTkd3Fbbu+Z1PVf2v60b442K1yumLiK6RZevfivNWuVYsuNsxlgahs/mD\n3Xj32JNISsiBFk2aHLYkJLiz4Hakw2FPhkVxnJGw9fna4HKmDbsdgKoF0O2pQ1vXcaiqH5oexoqF\nHxxxst/RXY265l1YUn77OT1gfb42bN/7e5/H21KlG6E7RIPr9EVECxXZ9kR6Smnu6mX3uCdizrRu\nqNh9+G9wO9Ij91ci2KxuJJ2u06ac01jqD3bDojhgHeRvxjQN+IOd8Hhb0NPXAN0IQyIJXn8HFs97\nHxJc7zWc6oaK2sad8PhaMLf4qhHfd0OqFzsPPOhvbjvYoxvhO5j57fH9FqZWXCVTRLRWkW1PFOVd\n4lqx8J8c490DwjQNNLcfRHt3FTq6q5GeUoKlFXdM2gS/He/+GT5/BxbOuRE5Gedm//28/g4cO/ky\nymatxfnGd3u8Ldi257c+X6DjkKaHbhPzUqYXIpqrKPZn05OL81Yvu9c1ET2ohqHhZMObqGveDac9\nCauW/sukldl3j/0DbV3HkZU+D7mZC5ExYIXB/n2n6lv2QJGtmFt89Yg2Yg2F+/DW/v/xt3VWduhG\n+OZ47tqfiYgo3aLYH3PYU1asXfFZd0piwYScl9nE8ZrNaGzbj/llG2G3JsDtypywvXwMQ4U30IG6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vV/cONl393zD1S/fYeeRTL5981omMNU4eq+tHefRw9fcM/NmVJgd2WCIctGXZbIpraDqI4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0J+p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GztterqJQybcan6Wh+lZfeuJOO7lVxxzHf00rqexpMAbiufVNf35aPtLS8PK6h4YQRPZ2c\ntPTKtQ8xvv6kgzKaLSseS3vnChwzA9qIO7LI3XxXyQcPLorjSTyqtjdYzMAYSo6/ksV330frlCpm\nnXckE+aewNXf+Cs33DiXMUPlfOPHz3H0YWP5zm3H0ryllefWtLJrax+O5SBdiRAgAaGqxGJhjjh8\nMj/4zEdYuq6LBd/+DZ/6wiwefXoz/uoqOlMay//wCLGjPk5w0MSf9EZd3k9K+ChITQ5uWUP5hHqK\nK6P4VS+QyqsP6rv5teTboTxajCRYpnD05XNpW9HC1id/RfUpHyMVq95D4KPw48BbNCxe/rukK527\npJSv/htu7/sVfzLNxCdWr3lozhHTPu1rbX2VoFHEqKrDDrihK12GE50EA6Xomu/An8/JkWZNhYnH\nzuDNPzzM+tmbqJg4kc2ZXUyOJ+jv9JNMuCQTLqBxQmU196/dwmGHVqMmUkjTQrg2g4NJAmE/WUch\naUMyoZEY1snGVcLDWYysjcjJsecNorviQY9WldDpK8lQHYPSSdOZOGW6F8wrUOqDCr/JH265j8Rw\nhpBvb5HOvDKWQEEgEJrCN6+5kAUPv8xjd/2d8z83JxdQqYDI/dvaYx8JdUSUQjedgo/cptULzHR6\nYDlw10Hcuw8kpJRvqqp+x9LX7/zG7Dk3hXb3QVFcSduWFwnp0YM27pQFu4kRil9+AajpLnrO2iGf\n5Ilbgr6sRkAzKfFlPJqfoqKg4eIUzJ/zU5MrBaabm88skQuERpr7/UmvwT/V08b29qU4rlVYvNaU\nT6WoaiKqre1hfp7yGbiuia5LxoRhWomC3DXAjd+5n5sm1DCwPkDLtiyJ+MiD3ZYuj7CNRmIcQxVp\nbHaR5ClaMaXDJIqZTAm7dpjsWgBHz66kT+3jG5+8hlt+fT0Xje1BFfCaHqGnOUi0P41UBN07ltI/\n1NbnuNbV/5Yb/D6ElLJHCHH5otd+cc/JH/lFSPiC2LpCNqCTihgEymyqRiUZVZ6lPuwpM1YHLWpC\nFkEtRlCLoVkWDO+C1CAyk4BUxlMOc11QFKSmgqF7KmKGH6TrvfCqmj41hKPYWOoQcRPSjkLCUr35\n0x6h+OX7RdjNigVFwbazaAeY53U9wJTGM9m49H6ajvsMrqrQq3hMHddNoytWTkQDgkND/Pi6+zlW\nlrF22d4tdstkNxYuF4umfV1PNjDAArZRK0McQxU9XRY9jwq+fFo9d173B86/6izOnXMYkOYZ3aVl\nSxFutxfEZndspaX99azjmB/5oCul7g85teqL3lhz3+Lq8inB3fsj/53QVAPbMQnFTYYGgwz2++mI\nWIQ0ga74sdxBqkIBrrrhC4hkHDc9jCIdlFAMQqUk7QGS1gCDWZeetE5/RqUv47EAhuN6wW4ilMzi\nS1v40l6yP58sEI6DaqsFH9M84q4P1/Eqq7GoN26nlmQo1RpwW5Zy04+e5hsTa7D7g/TvUtnRmt0r\nkHorzKxk87o0LVsFkw4J8rmpMVZ29vKtz36X+T/5GqfUpjAUHehhvVKGtVMjPJjBVQRlYw5jyeo/\nph3HvFhKuW/O9ruE9zyYklKaQoiPrFjxx9fLq6cGg6EylFwJcf36xxg3ejbvhH/aOOZ4trW9TOP4\n03HIBVKuROaVR6BQjVHckZsYSJo4moLvsM8y0LeKv9zyMMd/8Qymf3we37v9GcbWFfHJrxxHb3sf\nv3nkVeKDWTRVRwmWoSiK5+sgvIyn47p02w5PLt7Gk0u2cPjMej5/wzyef7GZ1xZtZ+zHz+e1P69F\nrTiMspSf4HAKPetgBjQyIQMroBIOeaaXumGzc8lijv/6J9GVkSAqqHkUP3+uQgUjCxcrp4CVtMGv\nSvxH11E6tpbldz9CZPxMzMo93bb1HA+2teU12d2zvtdxzGv/6Rv6AYBnPi0+u6154RZd+HzF4Wrq\nRu/d8Lg7OrpW09m7gUljT6NvsIW+wRZ8vjBF4Rr2N+Ha2p6ZTUfCmZ8/lx/efj8///lH+el3d9E4\nKUS41aFtu0s65U1swbCPTEaQ7ElRNCqNML2gZN267YydMYoBW2HYGulBCQ9lCQ9mPMXHvLu5puDo\nnrt5NqDTNRikNxKgt9Qzv/SqpjZFAZdJMVj914VceNHhlJeWIFBwpKcgKHARQsELCSlIrKvoKK7K\nxz56EouXrON3ty3gc9++AMXvQxEaaqFKbRVsAAASb5GlH+ppZsu6J0zXMT/54QP+7eG69veH+rZf\n1LL5uQkNE04rDK7u9pUolk1d3bHvbH/qSO+KprkFedz8/SqYnebmo/aERtYRBEpS6IofFd0LsqWS\nC4RGkjy790uZWTUnf64Vmvt9aZudm18hmxlk0rjT0TXPSstxbXZ2rWbV8j8ybsxswlVelS3v62Nb\nChFdMjGWQetyufbLv+Q7E2uwtmt0trt07dwzgH+aVk5iFBXCW9yG0SknwDTKcHOL0z+xlcmymGmU\nsuSVBI09RVx5VJxvXfpd5t/5bc6tT+NIeDGtkc4aZNKDvPn6b1KOk73oQyn0t4eU8i+aEfjEmmX3\nnT3x1C/5HM0TnAhVWlRUp6iryDIuCmPCDqNCJhUBlbBeRUDJ+TAlepGJIYgnIJGCjFkIpMgHUn4D\n/D7PSiIwEkwhFHRfmKBWhOmmAZesIxi2RKFJP2l6Ylb5fjyZ+03YukJV7WHs6FpJfe3RB/ye4WAZ\n40YfR8uSh6k/+mO4iqAXT6E3FB5CVyQlvlp+deVNjG42YG2YTHrPhexOmaSLFGeJ+n0eQwjBZEqY\nTAnbZZyH2MIRsoLxIsaSZ00+cVQjz/z2GXq6Bznnk8cDWZ7IJmjNRrGEw6qFP006jvWVD0Un3h5S\nyjWqqt/28rJfXTd39g2hg6lQ/jMoitRidjQTLJ9KfNBgIOqjQ8uiCgVH+khaWSqD2wnpQXRfGFVo\n2NLEzLSSdrIMZjX6sgaDWZVdKUF3BvpTCom4QSqhYSRsT5Uy4QVTSjrDlubnyJqJ3Pd08fui1I8/\nDdUKFhJzw46BP+CgCqgLW1QZDbibF3Pb9x/jnMoYRWk/vT06nR0mA337Jz/1yjQxfGi562dmJauW\nJencqTN5RpD/mqpz7Rd+wNXfvYwTJsQ8Oxe3j7V2OZrl4ApYsfCulCvdu6WUL70rN+Ft8J4HU+Ap\noaiacetrL99x/amn3BJUpcKmjU9RUTKe4rc0QR8IkVAFGXN4r/fz1SkA3mLyq7gSzXILD2p/cCJb\nzI08+4t/sGpcMUd95Bwy8X5u/e8lyFQaXZVEwwa1VVFqamOUl4coKgqgKALLkQwOZujuSbBr5yDt\nbf386ZkWEo9vo2RyE5HTzuLZX79Eol9j8tgjCfam0Ww3t2j1XobhoOmekt9Q80aqD52Eoe7u1g5G\nIaiS+FR3t4wuZB2FoCYI2RQqWXqlxrFfuYjVf36FRFc7kenzSCjeAsRVBJnudpYv/X3GsbMXfqgq\ndXpRKD4AACAASURBVGBIKXuFEJ/a3PzsX8479cf7NDeQUrKt/VVc12Lc6OMKjftjR88CwHEs1mx5\ngkMnXrDPY+QznIriFlRypOHj2DOP5Mm/r+frN5zL975xFzedVc50n8tdy9opSQSIDJYxI1rKG9t6\nOWVqGcJ1QSi0tHRx1DnT2DGQa+hP6KhJl1A8i92yhebtL6OpvvzJoyg65SWNlFZPJBYJkg7rJLp9\ndMeCRGNZqmqTNBWbVAZcFrd0M/mSI7GliUSi4FH6FKGi5AIpz7vF41gLVDTFQCA4ZuZUyiuK+O/5\nD3LFt8+nPJgXpsgr/dlApmBpEMejTGWtQZY9c3vadazPSSk7/s23+H2HXNb0wjXL711WXN4ULInV\nMbBzPcN9Le9IytfdrSJl+jRsTSHgswuBlKLIgpBEYZsC5U+lJe5jVHiAqGHy2sLNZDIWp86disil\nuTw1KG+8my4FyWjbVgiYJrrpMLBjLa6dZfK4M/Y4jqpojK6ewaiqQ9ncspCe/q3UTzody1BxQgoN\nTUNcUG8ypaSO6791E9cfN5bAljSdPTo9nek99tUhE0QxCoHUW6EIwRRKmCyLWUc/D7KFk2QtbISd\n7X6+dZaPW6/6AV++5Yt8vNFb+L5uBHnl3h+lXeneKaVcdNAX/QMMx8pcsXPLS5vC4w8vDx1xEpFy\nk6raJHXlJuOLoD5iMyacpdgXIqyVoFsWxFs8/5reATqau3hi4UY6uhMoEgxd4UtzJ1FSEvIMzt2c\nYzkglVxgr+Yof5qJrkXwq2HKA/0kLZU3N3WwfFkz9XNPxLZHAilFkTmlQa+/K1TVSFvzy9TVHHlQ\nlL/iojGYdprWJQ9Td/RHcbQoRrXDoaWSM8cEeObO/0U2J6nqraMlmd1r+5fYycc4OIpug4hSLyMs\nopM1so+51LFyaYqjx41m61/f4PH+OBd85QIgxbMBmxdu+6OZTQ29DvLug79zH1y4rv2DoeGOC1dt\neuyQQyde8K6sq0dXzWBD8zOMrm+ks6iIwZAfw3BQhQ0o2K5BwlaJ6hbxni6eWrCEi784BxuNpO0n\nYSkMmQp9GejOQE9KMNjvJzGsYyUVosl0IXE1vGsT23csYkLDqUTDVYVzSKR62bB6AbFYPZUTZntr\nyRKdmjHDzB3tcGhZFLo28/RfljI1GmCG5qe/W2Ow36Gvx9rn94pLk2dooxgfKWxM6VKCj6OoJCz0\nAv3v8L4wtx5azI9+8EdOu/g0Tj3jEHyqDfSw2VdCy5PPyJ7OtV2Ok/3Wu3H9D4T/SDAF4DrW7YlE\n94lvrvjf2VUl4/3RUOVBU072B8XxHuwyb4Cbr04pYg9J6zw0y8WXtgn0JdiwYSXVh87F8TXy9C9e\nQZEJ/GGdssZGKifWolRGaB8YYGtHP/bGHsxECiklUgj0UBAtGsEoa4JxxZhtQ/SsbGHzcx1k0p2M\nbjyRRgI0P/MHQv4YA8ld1M25gmTEh+NT8OlWgS7TuXw5R33uvEJVKh9QeRUqL5AKaJKX/rqY1s07\nkDlJSn80yHHnzKKqtIyQNfJ54xOz2fraNrYvvIuyEy4j5fqwzTRLnr89JV37m1LK5f/SRf8AQUr5\nlK75f/nikp9/6fTjrg8pyghFOpHqIZnup7y4cb+mvaqqM270/qsBTs5HJQ83Jzk64ciJPPTjhzj7\nrMl8+45L+N71j3DNTI3tq4bYFc8yZaiYhqIiluxq5RRzJMvquA42qqeyk1VIJTX8SYveNQuxh/o4\nfPLH9njo23aWnoFtbFv3OLZj4fdFyDoZSsbPJHn4objVgvoIjApZfPLzpzD/2kf43JUnMXFidS4Y\n0lHRkLmOPEWAzAVSQihoqB7VS2qMH1fPd2++mFvmP8Qnr5xLaXne5DR/PiMZLNcRDLsa2+7/Ucq2\n0vdL6f7pHdy2DzSklBsURb1i0fM/+N3s474Z7N6xksMmXviO92PvZpYqfBQCKU13UVS5V/+TIz0a\n8jDQPKwTtxQmxJK8+toa7KzktLnTcmIUe9ID87LTti08/n7WRjEdOtuXcfiUj+/3/IRQmNBwCp29\nG1ny6s+pmnoa0RnHMrNCckxlPY/85FccW+6neCBL36BGX4+9F93kNTq5gLEHvBZCCKZSykRZzAt0\nsFb2c0piFC89LPjq2aP5zU2/5RPfvoTzJir87ecPmunu7StdK/Odd3zRP6CQUg4IIc7c9PRPXzry\nsLpgVW0xYyu8QGpsxGJMJEvMKCGslyCGeyDejezrZf2K7dzz+EoqdJ15TRWMGVuBUBT6bJs7Fqzm\n8AllXHj8bpLfOWoehu6JUbiGp4TqWAS1IlzpEPMlaF29heYVW6mac2LBFwo85d2sphZ6u3RDpbbu\nGFo6ltIwauZBfdfK0gloqo+Vz/6E8IQjGXPMCcyuSrP6f//Givve5HxjEqta9+6T2iQHGE+sIDKx\nL7hSMoRJsfASZkIIZlHNsDT5K9s5UlbAtiLKB6uw1C3ck3iAj197AS8/9prsXf58r5PNXPRh9f/g\nIKV0hBBnrdv69Lryksbi2oppB9zGcSxMO03AFz2oYxh6ACldjL5BohGDwYA/Zx6dRhEOjhTELY2Q\nprHuzQGefX4TjWfPQQ8ZpHMU1WHLs50YSivEB32FV2g4m+tNtYjv3MSunjUcMfWTe/nkhYNlHD7l\nYjp7N7Ly5TvRy0dRftmlHFLmcnx1EYGudqyWNp57vZkfzBxHYptJakhjoC+Lmd17KGWlw1/ZzoWM\nJShGFOZ7ZZoX6cCRkuOpoVj4WL4oQV+3n28cr/LQ0y+zc2cfcz97IinbpXPtMhYt/H3KtbKnSynT\nex3oPcB/LJjKUacuaml9dYOwncpZh13+b6+NFpTX9vWeOyIVbkiVE6Z9noSbxhgKkWqYRzpkkNFd\negd3sPPpbViDuwpZWKGAQHo0PylxZQbp9uG6bTiOgl48hljZYdRNKicylCU4mKFl4z9obn+FcQ0n\no0Ri2D4dR1cKzduKKhHSRgjw+fPS0SNVKV3Ja/dLdjXvYrhvkKtvOK+QEe7qTvL4w4vp3DXErHOP\no2psXS4Yk+izxxIsKWL1gjupOPEyNrx4ZzKbjT/uuvZv/t3X/P0O28leOxBvn7Vi/cMzjpj6cT9A\n1kzS3P46UxrnHtCRe2vbKxw68cI9JinHsVCEVjBdLryfX2CiUttQxZp1XRw2tYZb7vg8t3zzTq78\n4mQmbHYY7NQw0zqt/SbOQAYllQHbRMkFSlaOOmVmVcK93WT7O5k2Yd5e56ZpPqrLJ1OdS2r0D7Wy\ndPV9iPpGauszNFR62dFBU8VXWs4Xb7yY5/6yiHvvfo1T5kzh5FMm4ggNVei4wskJD2hIIVHxDH7z\nqn9CCipKK7j1h5dwy00PcfqFM6loashJaOfFCexCg2vr3+43h3ds2Oya6Q89pd4hXNd5QNN8xy96\n/ReXnXvSbQetOmVaaRShYueofVZOyc+bs0DT8pL1ez4k88GUmguowKtQNcd9fPJrZ1EesMm6SaT0\n5rP8XLe797rriAKNJN2/g5Kiun0a4L4VFaXjeWPTAtRIH5fOGuCsMSb0t7J8ZSvfmz2WzJpesskA\nycSedJOUtIigFygm+4MtXVQEQgg0oTCH0bTLBA+yhdPlaF5+Ei4/uZ4Hf3QvvulT3BV//kfcyVjn\nSinfvlHgQ+wBKeUyoahXr/3VDT856eHbw1OK/TRETOoiFlG9giAB6G1FDnaxa30rd96ziNEBg+tn\n1GGYEpmxcfszCF2lJKhx8+mT+MWr29jcMsD4Rs2j/NkOuC7SthGau0f/FHhVz1K/w6c+ezxHXngK\nm4egv2BrkvOuzJtF5+Tbi2smsa59KWPcI1CVg1telcbqSZDCKNrJN44aZnRK4cY/vcE3x4yjdYON\nbe+9CF1LP+e/TeA/KLM8RSsVBIhLEz8qs6imWPiICIOLZSOvsIttcojTekejPVFCvd3BrV/8H/60\nYHnKyZpzpZRD7/zOfXAhpdwlhDjv5Tfu/Ns5J90aDAf3NpMdjHfQsnMJjWOOZ/uO14mGq9FUHxWl\n4wvU5bdDU90JtGx6nurSC8n26wz6fLl1aRrLdck4njVF+fTJfObOyfTaMDzkMVMsV5BOqbuJ+2gk\n4gb+uIUvbWGkbdK9HezoXMGhkz7ytvNtVdlE1nW8yJDs5jOzeplXnybgFiMzCRYv384pjeXIYRMz\nrZJOylx/995YRCdzGL1HIAVQJgKcTT0pabGQDhQpOJlRtGzL0tujMu/UGIs2bOLu2/uZd/kcvnjz\nD5Oulf2UlHLLAS/iu4T/WDAFIKUcEkKcvr1j0evjRh8brvw3SEznzXt3fyS6u/0feBWsfG+Vqwrs\nVIK1G/5MSaye4a0LURSV0Q2zqSypwdZKkP5SqPJ6BsCjvZiZYTKpfqIldXvsO9+wp3c7uMNtbG17\nmayZYEz1DC449Q6WbX+CMSdcQiboeUHpilug+MXbtlI+aWwhiMqr9438lWgKPP3gS3zt+nMJaG7u\nPUl0lJ8vf/Vk4mmXBfcv5vUnXueUT8+lKhrztp9aij92Ic9e9x13eEdbq2tlP/dh1umdI5eBOm9T\ny/OrIqGKynRmUKmrPZppE849qO0rSpr2mqSS6X5CwVKvyV8VhcVpflFquYJTLziWB3/xKFO+fy6K\nPsSNv7ych3/3KouNVXyu0c+u9RFCwmBg5zAViRQ4ZqHJ35Eedcoatti++CFmjDljr/PaF4qjo6ks\nm0Dw6FOoa+ihMeoFd30ZDZ/q4lODnPyxkzAUlzeee5PrvrmAiz99DNOnj0YKA7nbIlsIgYpHqclJ\nUqAoKkVBwfdvv4Qf3v5npg4kGX/0VBShQo4qqAqH7S++INueWRB3TfMcKeU+5P0+xIHgOOZXMpmh\nGcvX/emQo6Z9+sBPbaClYzH1tUdj+dRcr5SWU/HzKumKKvcZSKm7KfMZihdQmQ7ELY2BrEbCMin1\n20QNCYic95Ms9M3lPaKEI1FNhy2bnmX6uIOjJbbsWMSYM7/ECV+o4Pz6NOVWmHjbRqK6gpuyMFMK\nZkohldgztllDP1Mp3e9+n5c7GMJER5DBISx1jqWaqDAYLcJ8VDbyJC00yCi8UMaEqTE++9TDZtZ2\nz/pQcfKfhHR/lx0cOubxb//kovMevTrUENWIGjX4LBcGmrF37eT3f3yVzrZ+rp42ilDCJttqkvJY\nzmiGg2o4hbXAlceO5fq/reOnn48hDD1H9xuh/L0VCio+NUxlMMWgqbIrpRTGZr5vand1s7wf1tim\nU1m+7iEOm3TRQYkODcTbCR09h+tun8EMv86NV9zC1U3VOO0qmfTeMbibe2zvryqVkBZP0sJFNOIT\nauG91+lkWJqcRC0lws/x1NAmh3mILZznNND3ZIA/GkvSadO+Qkq5+oAn/iH2gpTyZUVRr3vm1dtv\nO/vEW0I+IwR4icnegW1Ul0/hkKazUVWDQ8Z7Cc10Ns7aLU/ulWTdF0KBUiw7jdHdT9inMuAL0Y83\nnduxLFbEJqLn5l0X+rLQ1+sv+ELZloKZVcikNZy0p/LsT5oEkhaZzha2bHyKmdM/c8DzMK00WnUd\n5912NheOSzI2WA8DOyBj8sqKNr40uRqnLYGV0Umn3b3m2zy6SXOiqN3vcYJC5yzq6ZVpHmEbh8ty\nJsSLee4vDsefESU2oZ+Tjromm0mk/1tK+djbnvS7jP9oMAVe854Q4vwXlvzs8bmzvxuIRfd/YQ8W\nbxWeAPag+eUDqbzko4KK3xelqe4EAGzHZGvry6SbX/IWpPsYWF29G0ml+2nI9cO8Fa7r4DPCjBt9\nHAF/EQA7+zYSLB3tqWLlXvkFiaJI+jZuZNJZxxUytflsbT6QUgTE+4Yor4wSDGjoiotP9XwywPOf\nCmgul39uFp39We7+1XP4YjGOuOBkDEVh48pFMrmzrcc1syd/2Aj9zyOnOnXisnUPLj/6kEvDB9vn\nZ1pp9hW/xhM7KQpXYeue5HS+GgMehdOVoPgNyqqLWb+hi+lTKhEILv3SmbSsGs+NdzzIedNdZvUX\ns2bnEKckUpDsRxE6ftXzf3AdwfD6RXQ0v8ZRDQcX+DW3v4YxfSZFdSauFHQkZUEQxa8phDTwqwoB\nzeWQk49gxknTeW7BKzy6YBlf+PJJ1FaXIoWLK3QksvCDzItSFKTUdYXrrv8ov/nVU8SHUhwx5yjA\nU/prXr6RRT/5n5RrmqdIKXcc1Il/iL2QE/45fWvbKyvDofJRk8ed8bYlGCkl8WQnYxtPIeHzKlK2\npqDqslBJz89bu6NA8XNHHujgzWGWC32A5RoMmioxwylUo7yqu8gxr/L7FSSHdtLW9jqN1UcfUOF1\nIN5Ohz/B6VdUcEG9RUQvhWySbdu7GV8TRVpOToUNLGvP895FkiOp2Od+TemQwOICMVIJiEuTl9mJ\nKyUnM4qw0Dmfsbwhu3lYbuXlNTvTWdyLpZRL3/akP8R+kWOvXNG2vqXh59fcc8w9d99gaKkh6G9j\n0+KN3PnH1/jUpGqmNVaTbrcZzio4loIQEkWTuSKTi8jYoCvofqiI+EmmTMJB/0gQlR+EQvFeioIr\nLSQSKb2EZcxwqA0p9JVlsS2F+KCBYwl8luOZSOeDKlUQiFbS0rGEonA1ExpOedvvmDGHeSOxhOt/\ndgEXNhjIju0ICRW6xi5H7DVOAYaxiLH/IG0d/ZxIbSGQAggLnTmMJi1tXmIntnQ5jdGMERHmST8L\n2MY6+jNJ071VSvngO7pRH2IPuK7zS13zj39+8R2fPfHIrwQy5jDDyS4a604ssEV2R8AXpb72aKR0\nEOLAS/IJ9SezaePTNEUu9qT1lSD9+LFtBdM0SUZM/Lpnom7Z3hozk9YY7PdhZRU029MLCJgOetYu\n9Em9tvS3RENVKAeoqLquw2sdjzPrxnmc1+BQ4supYro22DbZrE1IV8nYAscG25K4+yhMxaVJ8duM\n491RJgJ8XDaxmC4elc2cRR0v/i3FPUvWZ+LJ5NOmaf/HadT/8WAKQEr5nKYaV/3j9dv/31kn3BwI\nBfafIXw7CFeyO3l/d5rf7vS+/F9PO99F0fzYThYpPSUyTTWYOPbUtz2W7ZhkzQShfbid7wupzCCt\nA2sYN/szZHQVVxEIdWThoKgSK5EgEAuhCDkSSCn5oMrL3i76x5ucfOZhqMKrSImcj0u+8V9XHHyK\nRaDC5Zrrz2TxGx385Uf3UtFUx4s/uzdhZ7LHSym7/onL+yF2g5RysxDi9KVr7nkuFq0JlBWPO+A2\nnb3rCfj2XhAODu9kQulEBvdB83Nzzfm2C2d+/AR+dfOD3PyDjxANCeJWN2WTi/n5/bex4Nf38vCD\nr3J2TZFHXRlOMKqmguHeAQzVsw0oGXcUYtRMDD241zm8FcPJHgbtQaKzDqeschDLEnSkcmqRqkcl\nCGrgVwUhXSWqSwKa4ISPngzpBL/91d/+f3tvHidXWef7v5+z1d77kk4nnc6+AkkgAUTZZFcRkUUR\nZ3CZO46XGb1ex90Zl8GRmXGGOzrMjDr6A0VlVxCBKJvsIWTr7GRP73vXduqsz++PU1XdnXQnARIJ\ncN68DpWurq46VXXq1PP5Lp8vCxc0cs2HV6IrY06YStGQoni8CkWgyOBL/9P/+3J+/vPf89S9T3L+\nVeewd9sBbv/s90ynYL0/jJS+foq9KGev33rvuli0qmpm8xmThh97BrbTWLugnJWyI1q5LFnTDxVR\nMJqVKpf4+aOnY19QnkM1UICBgkptVKXS8EnpHr4UZdOd8uwSX5KqmMqM6WdwpM+XWRhmU99znPL1\nj/Ge6XlOrjWIa1UgM/T0Z2moiIInkb7AdeWEpVOTRfq3MMRixp/nK4TBe2klKx1WcYA6GeWdNDGf\nKn7BjoKD/1Up5QOH3emQIyKldIUQ7/3db9au+eYXbp31zb9Yof/Pj55gYP8A31k+E7/XYWhYxcyo\n5Qo9TZdo0aLduQKq4SMcHzzJoikVbG4f5vS6g/pUlKKQUjUQKlLa+NLDlx6KkFQYHvVRlea4wlCF\njdIVnENLQdkSviLwDZ1Tl38M1Zm44b6E59k81/5brr3lA3xoviDV14Ps7GVsvM2fIKA/RIGawyxC\nOw8TGIgJjUtoYVAWuJ/dnCJrmUMlHeTyWZz7HfzvHHanQ44K17P+ZiTTOWvVc9+98OKzvqrVVLYc\n9vadvW0kY3VHFDIQzFitSDSS2bOBpLYMXxFk/SjDfqSYeVKJxlyiMbdslmLbCiIjSZqB9flYEzbd\n9lBNi5qqVk5b/KHDPraUktXtDzHno5dx5dIEKxoKxLWGoNdQFiO/UiIdL/jRE7jOxCV+e8kwk6Pr\nF4OguuVMpjAoC9zFTtLY9pbBoQ153A+fCFVWx8fD8TXgevZPHcf8+9/98Vtmzhw46r/zPHtcbXLJ\nZn3sCW4yIVW63rOCJM2rsbTUVOOohZSUko27f8fcU69BFhfMpXKukpAq7gCqOpqVgvHZKUVAb8cA\nM2bWj7muaD0tNFRFw1BiRLUkCS1FdUTjvDObOP/8edz2rf/PtAvWhVLKHUf9JEMOi5TyOdezrvn9\nczeb/UO7j3j7msoW6qpnHnK96xZQI3GciIpaLPkcu1gtlfopusENn7mMf/jafaRzDo7v4kqbtNPL\nBz51LXf99iY++q0PwrxW8H1OXtTMns0dRNXAMEB6Lon4kQMVjltgy66Hqb3gOnxf0N8Tp7crQV9P\njJ6BCF3DGh35QFx1m9Cdh46coMdUGSyomHqK6z53FYmGOr78+bvp6O7D9k1s38TxC7jSxpMuEr84\n4FdHV6JE1ATXX38hTfWV/M937+KLV3/HLOQKH5NSPvaq3piQSZFS7nM969zn1v04s7dj8qRJe/c6\npjQvwzFU7KKgKmVNxw5XLjEq/EczU86YrTBmJl7BC+ab7M/C7rTCzrTOoBW4+SmCcglhqRfFMJKH\nLTux7Bxr9j3E3M/cwNkLCjTGgkWsJKj30nUVVwaBNqFINE2gaUfXNwbQj0kjEzv8JYXOlWIWLSS5\njW18i5dME/dfben921E/QMhhkVJm0+n8Obf858Pd7/rA9/25rsffNNeT3+PTty/KSI9OdlhgZhWc\ngoLrCDxbQfoiEFhj1gJSgqoE2aeSAUV5kK8aXPp4eNIpbi6Wp2B5AkVIqiNQlwwGB8eSbnEt4ZfX\nEiWap69gKH1g0ufk+S7P7Lufc79xCR9ZopLS64LH1zRE8cteCImmH3qcCg5/7AomDwyUqBFRrmUO\nAxT4OquddrKPFfBuOBEWpW8FpJS+45pXmoWhF1e33VbwJ1LFY2iomYt6FCWhJWZOewcHuteiDQyV\nR514g4LB/iiDfVGGB6MM9sVID0ewLRVNk3iJYH0bywX259GcU54ltX/vs8xtOfuIfVvrOn5PxUWn\nceH5ldTHgr7T0nk2yOoGlVxCKVadHGZJncGmgsP3mE9ENRF8pN3G4J487sUnSun/CSOmAFzP/mfb\nyX3z4VchqAp2hohx6NBQGI0aHbKN6ZlSfMm2PX9g4eyj6yN5Lexsf4bm2WehRGLlBYKviHFCSuAV\nz++j57Kx5X4wWo2giJIVsSj/V3JT0xSDiJIgqqaIa5U8/9hOvvypH+Ucyz1fSvnicXuSb1OklL91\nXOvaVc99t7B19yo6ezdNeDvXtdi1/5lDBLuUEonEMVRcXR0npFQxukj1pcCRgqrGOv7sxku4+Vu/\n4fvfW8XDD7Xx4588yiPPPMtI1EHOXY6YsQhRXcPSZfPYtrmLhBYINL94zByJth0P0HLWh7Er43im\noK8rRn9PjN6uOL1dcfp74/R0xunqj9A+otKRh64xoqq/oDJoabQuX8T1//dK/v3fHuORRzdgeyaO\nb+H4BTzp4Jd68qWHJoyyoJo3ZwZ3/Pgpy8wXPun7oXPfsUZKudHz7LOfW/fDCQXV4Mg+qium4evB\nQHGnmJUaK/Qny0yVTFNKgqq0mV6wlcTUkA0dedifg51p6MoHs6l8WcrWBzN8joTtmKzZeR9TPvEX\nLD/ZYmG1R0L3y1kFhEJtbYq+rIPQg2CFqoF+0CL1cCtIC48o6mFuATVE2cpQPof7A0t6Xz3ijoe8\nKqSUPTnTWdm2e6Dz0acPuH27I/Tvj9B9wKevx2VkyCOX9SiYEtdWylFxKcW4EqPuTIHG6lhJtQdG\nFIo2umkGnj8qpFzpYXkCy1PwpUBXoMqAqpoC8YQbmAapyrgArlQEUlOQkwga33d5fve9nPyZS/nw\naVXMrYxjKNHi4wfugCKqohmSSOTQ+4ihkmfyeT1Hi4vPc3SbGexVBbwrpZSv/05DykgpC45buLCz\nd9Pap1++9bCCKp3tnrAEcDKEEJw87/20bbqb2EC2LKiMYZfcoF4UVIFTX8HU8P1gyLosOlvr9uj8\nPmekj2yul7rqwzuZtu3/A/7KhZxzxTQWVftU6F6wfpH+uBJZCcXAFZMGBAAcfPSjlCB/lJ2skgfI\nS4efs8N+kZ6dFt4ZJ5JJygklpgBc17rZsrPf/N1T38ins11HvH0m10sq0XjI9cpBkaKJzCeCyc4+\njls4Yj3+a8X3PYZyXVQ1zAtOsmP7pYoLEkWROJlhotVBb9XBzlYwKrLU4gKjtM4IBqQGW9meWtEx\n1BgP3b+Oj133/ayZty+QUr5wXJ5gCFL6D7pu4Zp1W+4xu/s2T3ibrNnPjKkrD7l+JNNJZXIqVkzD\nMdSy1fTBUf+SqHJ9Qc2UWj77jWu5/CNnE69KccqZC9iwoZ2/+/u7ePmVjQxoeXKN0/BjNpbloCky\nOJEmktj+4UeKZXJ9RKIVJJUktV1ZaruDLdZto3T5mF1aWVT1dsXp74nT3ROjY0hjfy5YIHfkAlHV\na2pYeoqPfunDtPeafPemB8mYI1heviysXGmDUAGJgsLal/ZwyXl/b5p5+2Ou4/3idb0xIZMipdzg\neva7nl33w/TOfX8cd7Lc0/48M1regR1Ry3borqaMmk6oh0oPXxbNI8YIqsmyUuXNEQwXBD1ZkUj6\njAAAIABJREFUhQM5wa4MDNujM3xK58nJhI7tmKzeeS/JD3+KWYs9KnSKC1+BJ1086YIeZeHCaWzt\nHEGJ6+hRHz3qE42P/+qLoFKYZC0ZQzvs4rVfmnybNfk09i0F6X5h0huGvC6klN1Z21vx/Y37Or/y\n7Fan44DF0IBLZsTDzPvYVmA+JYu+EiUUhXJ5R/tQnmkNqVEhZeigGcGmR5GieOz4Lq5vY7pKsHmC\nvKtQKK6Ho7qkospCqQfHUMs92WPXHYpQgoXmGDzf5dm999H6ifdy3ooqqowgC+bjgx6FeBShqxDX\nMWI+ieShIr6WKH28NvdnX0rul7v5lXyFr/GivZfMYybeFaGQOj5IKU3HNS/o7G17+cnV/6/gehMn\nUTL5V+9REzGSLJ5zGRvbfkV8yCQ5bJEcKZAcsVCGfXKDOsODEYYHIuSzGq4TuLJmqyK4moKXz9DT\ns5lNOx7kpHnvO+xjtXU8QfqUeSx73xyqjGAd4kuQePh4QVZV1YLPk6qAoaAZMth0gTKB0kiik+Xw\npbAAT8tOqjBYSh3/zDrvObr2mHhnSSmHX/WLdhw54cQUgOvZN9tO/v889NQ3C32Dh3c6HM50kKqY\nOuHvxmajgAlnTWXzfaQSE9cXHwv2dL3I9JYzyiLKLfZLjZpPBAuH/MAAifrqcX87YWBWytF+2eLb\nJ0Q5P1X8v8KXPv9D72M3/FPGzFvvDIXU8UdK+aDrFd67dc/v8xu2/fqQA21oZD8Tmat09rXR1LCk\nmAEIxJSm+eiaHJeV9KXAKQ7ydfxgwRitSDF/2Wxqmxu59EPncMn1F3Ln/Vv43Bfv5nNfuY27H/gj\nviJxfUFUlxBRkUcI9q/dcicNVXPxDuzC2HuA6o4Rarty1HdkAnHVlaWmI4ve7WJ2aXR3JIrCKkFP\nd5yu4fGiqjOv0GdqrLjsHbzr/e/gS5+/m73tXaMlf76NJx1A8MBvnuHiC7+Yz2TyV7uuFzZBH2ek\nlBs8zz59ddvP+9Zvu8+VUjKc7iCVaMSLRsp26K6mIPXx56uxosr3BZ43XvRPJKScYpaq4AgsO6jt\nz+d0smmDgb4ofb0xhgaCSKrrjhmyrukcvAixHZM12++m+v2fZNo8jaoKB1UEA8xNV8GTNo5fACNO\nJFWB6YNSGUFNakQSHqmK8YvU6STZT3bC16maCIMcOjQVYJ/M8HestjPY3wgzUscfKWV33vNO+83g\n/u1/f2CdNTTiYJo+tuXje3Jcj5EQEiFkWUh5CqApiIgBRrDwE7oRDOzVo6AauL6NK4Mgj+17mK5C\nvrgVvCB7WmoB0XSfeMLFimm4+qFLKV2PYzujosfzXV7YcRfN13+AZWemaE4ExlKlbD1GHBGPcdL8\nKWxM54kkPJIVCvHE+PtWhYJ32FwqExodATxBB3OoZAP9Zhbnl4VQSB13AkFVeHfv4I4/PPrMTWbB\nzhxym3kzzn1N952M1zG35Vw2bLiDyHA2EFTDhbKoig/bMCjxMgLPDM6pVkzH1QRPrfkBQgiWL/7Q\nYUe6tO1dRW7hXBZdtITaSqdoLCTK5a+e7+ILgs+QodM6rZo9WQsR19AjPrGYQjR26OejhigDHD64\nOyItRrCZRxW3s83so/CChb/iRBNScIKKKQDXs3/ouPkrVz13c25f50sTd7ARNB7HY9WT/fqIaFoU\nzzuyOn4teL7LQPoAFY1zAqc2fbRfqmS0U8pAWCNpYtVVEwuosRxUOjC2flog8DzJpz99i/kf//lA\nl2nai6WUG47lcwqZHCnl455nn7Fp528H23Y8YJe+0IYzHYcsBksUrDRqqhorpuFFFAwjGN48VkiN\nxUfg+oGwcsac0PKuQrS6kguuu4APfPZazr7hCrYPGiy5+nK68kF5iqb54wwuJuL0U/6cfGGQ4UwH\n3T0b2bb1AbZsuItta37J/tX34m9ZT217mvqODHUdGaq7ctAhGeiI0tsVp7sjSU93nI4hrSyouvPQ\nldeINE7lE1/+ELd+/0meeHIjBS+L7eexvQK3/Ptdzoev+85wNmue43n+Q6/7zQg5KqSU21yvcMqW\nnQ/vfHbtD/Ov7H+KmTPehV1y8NNVPF2ZsE9qLL4vcNzgmBxb3lfaHF+ME1Elq17bGp17kk0bDA9G\n6e+NYVvFoJMqSCankMn1lh/LdnK89MrdTLnoBrQpQUVB3lLIucFQypyrkLYVbN/E1zSIJJkxs4Hd\nWRulJkos5VFZrY37gp9PFdsYmvC5tVLBbtKHXL9RDsh/5OV8Ae/DlvT++fW8DyFHj5SyL497+gZ/\n4MlvWy9nh2277HIOY3wkFFA1idAVRFTlt9t6eM/pLUH0PBoJNj1a3nxVwZU2rh8I8ZKQMr3gMm0H\n83rSTnC8+V5QXaLGJPlUBFdXxvViu56FVuyDcVyLZ3ffQ+yK62maV4kqgmM166oUPB/bMyFWAdEK\n3nPBIh7a3UekEuKVHjV1h5oSJNDJTNIq0kicbiY2620ny4/YkhvEusnE+1g4/+xPg5TSsp38+0cy\nnT/87ZNfz2Vy4z3Adux78jXfd1VFM4tmX8r69Xeg9veTSNsk0jbJ4QLxjEUiUxJYVtAnZbm4EY3p\niy8ib6cnte+XUtLW/jjO7FnUr1iOokgKXnB+L3hg+cG6o/SZQYsijDiXnDOfR7b3oCQNjLhPLCEm\nzLAGAaxDheVYSqWA/8Ca7C7S9+dxz5NSHv6P3iBOWDEFIKV82PPss595+b/712z6hXVwzantmKhK\nMOzrSIvEifAVgRFNMZLtnDSS83rYsnsVM2efi6cp5RK/cf1SYxYmdmaESOXEvV/jOGg/5ZgIVU/P\nEGe/67PZ229ftcY0rZOklJN3wIYcF6SUbZ5nL23b8eDOJ1bfknOKx+js6Wcdcttsvo94rIZCQseK\n6URjLkbEw4gEdubKwYNMxzT5+zIQVZYnsHylHCnKuwqDlmDQN6g6bRl7rAgHcsHEc98X5VlpkxE1\nUsyYuoLW5tOZO+NcTp5/BUsXfpBlC69iXuu5FOwsmzfdw44X7yDf9jx1+4ao68hQ35FB7fAY6oqU\nM1Ud3TEOpJUgU1XMUo2IBNf97bWsWd/NT37yJIOZAT76ke/mv/aVn+43TetUKeWaY/2ehBweKWW3\n61kr9nW99Mz+zpcKWZkru/h5RcOcsWKqdN4KbMbF6GUxQ+W4IsikusFm2QqOIw4RUSUhFVyqxSGS\ngagysxq65aF4kkRlI6XFh+tarN1yN63v/CiyprY88yef0xm2YciCtK0yZGlYXo6Cm4F4FR++5p3c\n8cJ+tIYEsUqfRKVHXcPoItUQKjZ+eYbPWKpFhOExmSlfSn4td9v/QduQjX+hlPK+4/wWhRyElDJf\nwHvPPrI/+Vvz+dwuNx0MktYFqhZYo2uGj2pIRFQDQ+W5fQOcsWQqRA2IRhCRCGhRMOJgxMsiyvEt\n8i5kHZWcq5BzFDIO5ItiPe8KCqZWzKAGS6hStUkJxZP4bmCO5XkOq7f9ippLPkr9rCDwW/CCY3XY\nUhmyVApeFkc6EK8iNrUONR4hW6GTqHaordcPKZNaRh0vM3Fp2GSBgedkt3yWrkIW55O29G4KzSb+\ntASmFIXPmubQFx584uv5A93rStcz0YDfV0MiVsOyRVezZdcjDOxfTzwTzI6Kp23imWBLpC1iWZuI\nGRinNM1+B1lriKH0xBNHNu36HWrrXOJL3xmUXfsCqxi0yjmQdYIqgOAzUyh/jppaG+jM2ChVESIJ\nj2jCI1V5qJjShYLDpHkSANrJ8Wv2WD2Y/2ThXS+lPD6Zj2PACS2mAKSUaz3fXvzKvifXPPrMTaZZ\nGO03233gWVpbziqfxKQyfrF48Amu9Ds55nohFGZNO5Otux89pvu9p+sl4hUNxGunB4OBi1mpifql\nFEViZ3NEK5Lj7mNsVWKpJwEhJpwx+PxzW1l68qfMTW17vp/PW+ediGnQtwtSynbXs07t7tt6728e\n/5K5fc/jE1qe7j7wHLOmnYkV07BiWllIaZqPEZQdl63xS/gyyE45RUMKV4pyg3SpFMV0gwjqsA3d\npmBfb4TO/SmEKZFCHFLHf7QYeoLpU5axbOFVLF34QXQ1wuZN97J79Z1Eduyi8UCaxgNp9APuuExV\ne1+E/bnAvS1w/dM57yMXYUqDFQu/ZD/68LpH8nnrFCnlkS0RQ44LUsqs59mXmtbItx955ItmT/em\nclZK6qLcyzcRBwsq3xc4TnG4qauU7Xpdt3jpjMlOWSr2mGyVZaqoOZ9ExiaWs1FdH8NIYjt5HMdk\n3fb7mHvKlUSNJLrtIUxJNhMIsJG8yoAFvQXBoKUyZCkUvCyeblDZ1EyipoJ2CerUBKlal/op4xep\nJ1HDRiY2PjJQMKVLVjp8j/XmKg5scvCXSCmfOx7vR8iRkVJ6jvQ+k8b++FdHVucfybVLTRcomkTV\nZDATLaIg4hp/2DPAhadORyTixayUESz+IkUhJZ2ykCp4NjlHJesoZB2FtCNI20FGKu1ALquVxVTB\nVHGswIBCKqJc7ieKFtF5c5AXd9xJ8+lXEolXl0tbR/IqwzZB4MvSyDgOppeGRA0iWcP/+sgZ/M/m\nTuJ1kuoGn8ap48uwGkWcXswJxX+NiNI/pnzKkT63yW32z9je6SJXSil/dXzfmZDD4Uv/VtcrXPD0\nmlsH1m65yzULQ9RXz3nd92vocZYvuoaClaZt450w0Fd27guG8wZbxHQxTBfV8Zm96FJ2dDxD75h2\nGikluztfQFTXUjV9Carjjwt+5V2KwYbg85F3wfZNXEVCrAKRSHDSoqmsH8mhVUeIpjwqq1WSFRP3\n//XKQ/v/pJQ8LPf5/8WmjIV3uSv9b5/o4v+EF1MAUsp+xy2cM5Ru//dfP/YFc3/Xy3ieQ9bsI5mo\nLzvklThERI352VcDwTXWCKK6YT6JRD3rtt5DOvv6RjBJKdnZ/iw5J82U2e8YF90d2y8FjMtMuWYe\nPT6x/W5JVPlytJTBk4ElpW27fPNrv7Qvv/SbmeGh7FW5XOErYer+jSdw8jH/3LTSN27f+4f81l2P\nemNFjOOY+NLDqanGTBqIBERjXjAfQg9mOelFQaWI8S6PpQxVaRst+xtt7i+Vo+SyGtm0QXrYIGK6\n6EI/JmWtQig01i1g2aKrWTTrYrr6NrN59e04m9ZQ1x6IKnHAp6c9Tnd7gq6OBPsHNfZkYV9G8ouf\nPuXf/MWfmZl04Qu5bOEqKWXude9UyOtCSul7nvMdx8m/5+WH/3F4xxM/slzPGZOR4hBBVZpjUhJU\nrqMcurnKqJAqXo5mp0aFlGMpGLaHbnlEzOBLX3N8dBHBcU2eWvMfzJlxDsloDYbpEssGEVcrrZIe\nNhgeiNKTVegzocdU6TF1Mo5J3h2BZB2f+tSl/ODxnWjTUiSnSKobPabNGC1xmUcVWxmasErhVBq4\nn918hRfye0j/pBA4SR3ZISnkuONLeZcl/dN+0LNl/2e3r86nfQs9EhiNKEkDL66xakcPl5w1B+JR\nSMYR0SREgs3XNBy/UOzjNMk6KmlbJeOojNgKw1aQkRq2IZcLRFQ+q1Mwg8VlxHQDi+ni8N7A2Moj\nk+9j066Hmb/8QyQqm4gUXKy0Sjatkx6O0FcQ9JrQa2r0mTp5d4SCb0KqgabFMxEVMTqSKhUNNlOn\nGxgHOfudQi3rJslO1ROjW+bZLzN8nRdzq+l5wsJbLKVs+1O8JyGHR0r5vOtZi7bveWzNo89+pzCS\n6Thm9z1r+jtYPPc9vLLvj2zb+iBqNhfMk7JGHfwMy8OwPDSpsvTk6+jo2UDOHAQCN18tUUXLnHNR\nXR/N9XEsJTje8xqZgsJQMbgwbKsMF6sALC8H0QqIVXH1B07lzrWdKHUxEtUOqWqfuvpDA8qn0cBq\nxq+5B2WBm1mXe5C9W238k6SUq47Zi3McOSGG9h4NRYHwJSHEg8+8/F93VSSb6k5f9nHj4MxTWTQd\nRdmfAniKQPiSppaVNE45mT17/8gr+56kKjWVqQ0nE4tWHu3+0Tu4g73da2huOYOmqQvxNKXYd6CM\nlvqpY4b1jmnilr5E1QQlk95S9sk76FIymqXa0naA//WRH2V7e0ZeNE37eill91HtbMifDN93fyKE\neHrdtnvv2tP+/JyzV/zvZDJezyv7nmTujHPIpwzyKYNYccieEfFIaBBVIaqVBJWc0OHRL5pRlEwp\nbB8K7qigsmylHEG1TJWEWcC3C+U6/mOFrseYP/N8pPRp717P5tW3MbXhJBpal5OoipLOxejMJcnn\ndIbaO7jzB7dkB3bsOWDnraullBPbH4a8YUgpnxBCzO3euOqn/TtfOHf+x76ajM+dc9h+qcmuG5u1\nKgur8qUoiytpgeF6xS99F9X1ywtU3/UQQuH0k/+cLbse5iT1fcRStSh+cei65wdDK5UIiirp0E10\nRRJVNSKKT0QdQjMMklNbOe/dJ/Ob3d28b2YllSNDNE2L09/rkM/5CCFYKutYSx+njhl6akqXh9mX\nX02v5eBfHc49O/GQUm4VQix4fqjvu+c98cRf3HrO4vgHpjQiEhr/9cIe/vJ9SxCJWOCWF4sVs1JJ\nMBLYXhbbM7E9k7StkLZVsq5yiJDKmkFWKdiCPj/d9IgUXCKmg24FM3s0x0cIhWy+jyULriDpRbBy\nQQDLVwSZ4nGqaT5RtYCuQFTViag+mhhAizahVUzhMzdewBe/di83L2+lNuswbSDC7h2jGaf5oppf\nyVc4RdahHWSrvZJGvs9Gbw9p08G/UcLtJ3pk/+2GlLJXCHGW61l/8/Ta/7ppeeEaY8HMC7RXM+90\nMgw9zsnzLyeb72fjjgfQtSitzWeQKiYfDmZOy9ms33Yv0xqXIgVMqV+M4wZDfSOag6spFCIa+ZxP\nNO4ybFgkCpDQFJK6SlL3MJQchh5Dj1cRqavj1GUtvDCUZWV9lMSIS21DhP4+l2x6NNafFDqu9DGl\nSxSV5+mWP2OH6SO/5+D/w4lc1ncw4s34+RJCJFRFv0VR9euWL/2z+IyZ70Rq47M+chIxNXa43tgh\nvjDq9id8STrdSXdPG7Y12usWJAckQqgMDe8lZw4wtWkZSImPpLZ+Lg3Np+AbBl7JUrh4aUcC62sn\noiIiYEQ8ojGveOnyyn2/YMUnryQe8UlokNAgrkGFEfw7qfvENZ9f/Ou9fPyvL+Kn3/uN88v/ecqy\nLPevpZS3hSfKExshhCqE8nlF0f5u0exLIlJKde6Z1zPQlMRq0KmpL1BTZ1JV4VAbCWaZVBjBMRDX\nfHRFoiuy3EdVykqVTCgKngia74tf/MM2DGU0hgeDeRNOv4K/9gW2PHYr7zv320cczvd6kFLS0bOB\nrr7NtEw9jWTrSQxXauzc+Xv/wBO3WfjuP/uu+6Y6Ub5dEUJcq+jGD6eceWF03tUfNWKVcTQtMEk5\neOh4uZeqJKTGlP65jnKQoBoVUp4jyhkp3Qqi/KVIqjvQzdNP/zOLZl3CnBlnYzs5Xtn3RwrWCNOn\nLKemcQH5lEGuwiBTE0OtkdQ1mDROzdOalMyugJkpi5aUpMpowshn+fqX/4tPnDaVpt4swxsKtG/X\naVs72rB/p3yFK5iFgcJL9PJzduRtvPts/BtPpLkmIRMjhHhnXFPuPHd2fdWNFy+Itw3l+PzHzoKq\nFFSlEIkaiFdBvApb2hS8DAUvS9r2GbZUBiyNIUtloACDVjATbcRUyKYNshmDfFYjmzHQTa9YNuWU\nj1fD8hC2w9PPfg9Ni1GZaqKhZh51TUvwojr5lIGZ0Bmui1NZa1HXYNLUUGB2CmZV+MyqsJgSj1Fl\nTEH072P1o8/ywh/a+GRtFd1bDbauc+nrGT1tdskcGxngYtFSvm6HHOanbM0NYW208a8Ne6dPfIQQ\n8zQ1cldFcsqcM5d+IlFb1XpM79+ys+zrfIl0tpulC65E0yLje/x8yYsbf4YQcMr8D6BHU+WB7UFP\nt0a2KoqaklTVWtTUmdRXOTTHoSUJU+MOLSmLSqOKlF6HGO7CHzjAZz7/K/7povnIHUP07zXYvwN2\nbhvv4NcvTR6ng92kc/vIdFt4V0sp1x3TF+BPwJsmMzWWYknQXwghfrx2/e23b9/5SPOyd/xloqp2\nVllETdpoP+Z6WRRPHkWRNebViEWmM7N++oR34fseQwO7GB7Zz8x5F47enyJwitkxT1PwVVEUVEEz\ntyz+Tlf8cT1TYznYvX3Ubljg+tDXPSzfs/yLliLEQ4WCc2OYjXpzUMys3iyEuGfb7t/frmnRpfrC\npXF13tlUVtgkUzaJpEtKD8RzQi9mp1RZFlLamIBVSVCVGDvfp+SgNra8yrBcpK9SnWo+rA3qsUAI\nwbQpS2luPIV9navZ9vi/0j+0y7R9a6NvF26QUm47rjsQcsyQUt4phHisd81T/9r70hNXL/zIxyMz\nzj1/3MnV98S4S2C0f8ob7Z3yPTGpkFIdH9UNNq0Y3Tcsj+xID/FoDS1TTwWCvr3Fcy5FSp99nWvY\nt/5nLJh5EVUNU9Ecn7QTo9eLB4JuWg6QqMJAETZKspeqRBNf+/qf87nP/gf/csUCKjMOtunTMhRh\n/57AZOIiWribnewjk+sk32vi3iCl/OOf6CUPeZ1IKZ8RQsx5ak//Vx+79em//dtrlysFXdeiyTgi\nVgHRJEQrcKRTLE/Kk3U80rbGsB2U+JUDUgcJqWw6yEwZWbeckYqYbllMaa7Prj1/RCJZPOc9VFdO\no6d/GxvW30Fj7XwaZ6wMjFV8SdodLetXRAFQUISBKvIo9FFZ28LKC2zWtbWzWrosb7GYbcbJpD0K\nZlAy3iQStMlBOmSWODq/5BWzjYGCjfdXEu4Kg6xvDqSUO4QQy4fT7Z989Jmb/qW1+Qx9+eJrolHj\nKEzJjoKIkWRe63nkC8O8vPlXLJx9CRXJYD5rSVTVV88mbw6yZdcjTKlbREPDwnH34as2eQyyul4O\nqBmKR1SFiKoS03R0ZQRV6CRSDShOgRs/cTbfv3c1n13STCqbpSETZWRILwcETOnyGO32Y7RLgfim\ng3/LmzXI+qbMTI1FCKGA+Liiav/S0LxUXbjiI8lEzTRg8uzU62WyEsKSgCv1cJX6pDxNwdODwZci\nElhUB2YDfjkztf2eX7Lik1cSMUYzU6XsVEqXbH9mPbd/547MQPdQRyFvfVxK+fxxeXIhfxKEEJcp\neuS/E1NbK0/52J+l5r5rJjVxP8hKRaBCh7gmiag+EVWiCTnOiMKTHGQ+IUg7kCkuAAYtwdBAhMH+\nGEN9EWp6c/Q+dRcLGt9BNHJsTtBHon9oFy9v/lWmf3hP3vPsvwJ+HX65v3kRQpyqRqM/jqQqZi/+\n6A2p5tNXFkuTxzM2M+W6ykGZqVFTCsdS0IqlJLrtoTle+d8R00XkcrS13c1pS65DiInPuZ5ns33v\n49hOnvmzL8JpqGO4Pk6mLkpDU54pzVlmVfnMq4TZFRYtSYXqyFQ6N2/mln+5i+9eNhd3TQ9dm3W2\nrnPZ1D3CfezOraMPD/llCf8ZzuF58yKEaE3G9FsNQzv721+6PPaJj1+mGNUNuKoIMlJulpxrM1DQ\niqYlGgMWDBRgwAqEVHo4UrTuP7yQUh2PLdsfpLZqJlMblhyyL529m+jo2cCMqStItSwmWxlhuC6O\nUg8NTXmmNuWZVwFzKj1mV1hMiVdQoVQju7fz1S//nOtnVjOl3efA1ijrXhxtMR2WBW6hzesiZwM/\ncPC/JaWceGhayAmPEKJaVY3vAh9dPPtSddGcSw1Djx+T+3Zdiw3b76euehbTp51evr5UnfXSpjtY\ntvBquvu30tW7iZMXX4WIxnGKGapcRYRsZYRYlUtVjUVNvUlThUdLEqYnfJoTNlMTUKE3EPUEDO7n\nx7c+xGzN40yhM7LdoXu3wca2LL/N7fceZK8l4QEL7/+82RMDb3oxVUIIkRSK+jdCqF9uaF2hzTnt\nmmiivmXcbY7WPv1gM4tx96EeuZ51rCGGW8xIlQZfloSUpvsYRvBv3QjE1Mq/+OA4MRVTfPa+sIGH\n/v2uXH97X28hV/g8cH+4IH1rIITQgD/TopF/bFwwM3HJjVclTlk5n5qoIK5JYlogpHQh0RSJWjSh\n8Iq26E5ZTImi61QgpgYsGDAFg/0xBvuiWL0q1T059j32E05beM2r3k/Pd/E8GyEUkBJNi066uAXo\nG9zF+m335PsGXym4nv1V4CdSTjIUJeRNhQje+Mu0WOx70erqaYuvuz7RdNoKVF2ZsLxvMiHlugqq\n5aM6QW+UUeqTKjZJ65bHlo13M6/1fOLRqiPul2Vn2bp7FYlYLdPnncdIQ4KhhgSpJoep0zPMrHVZ\nUAVzKixaU4Gg2vb8S9x9+6N87ewWNj+ym28+dsD6XVen6yP/1UV+Lyzpe+sghFhZkYr9SzRqnPqN\nb90Q/+B1KxC6UxZSw7bKQGG8kBrKqUFGKm2QzeiYWY1Ysayv1MivF7Oqim2zccu9tDSdSl317En3\nQ0qfvR2rGRzZy/w5l+A2N5GuiWFOMWhoytM0LcfcCsmCKp+5lYVAUIlK3I6tfO5zt/GFpc1EXvHY\nu0nnufVDrOKA+xjtLnCHjf8NKeXEPtchbzqEELM1LfodJJcvmfseff7Md6sRI3nkP5wEx7VYu+VO\nlix4P7FoVeAqfVCry9DIfvqGdjKv9XxMK83G7b9mTsu7qK6ZhRXTMBNBqWq2KkqkxqOqxqKuMc/U\nlE9LEqYlPFqSNo0xjQqjAaNQQA7s40tf/iV/fcZ0jJ3D3Ppkp/y3zTsLjiefLOB9QUq56Zi8YG8w\nbxkxVUIIUaFokf8LfCZVP1NpXXFVqmb2aZSa+g4WQ2PFUrlE8DBiSh5xqu74vyv1cal60F+g6X5R\nUBXTpIaHl+2lZ91qllz5biKGj26ZbH/4j/4LP/ut6ZhWl5nJfw24W75WT+uQExohhKGoysf1qPF3\n1fVVyWtufF/qoitWUhnX0Mq9UkFmqlTy6UuCgb2+KA+XHLBg2ILeAgykNfp74wz2RYnPfC+KAAAL\n+UlEQVT0Omjbt2Pt28rslncd1T4NjuxjX+dLzJ1xLrsPPEdtVSuGHqNnYAfTpiylvXs9UvrMnXEu\nvu8SiaTY3/kSbTsezGTzfQVfejf5vvvfUsrDjzgPeVNSFFVX6InETYqmt8y9/P3RlnMvVPVE4qiE\nlHDkaFbqoD4pw/IY6txCLtc34Xy2w9E3uIs9Hc8zs/kMojMXM9CUxGtWmTo9y8ymAguqYH6lzexK\nn2qjme//4+38948eLbR3jbgK/EfO8W6WUk48vTfkTY8Q4qyq6sQ/uq634vpPnqNcfsOFhlZTx5Cl\n0WsGPVIDFozkVdLDEbJpg3xOw0qr44SUYbpobhAMELbN+rZfsmDWhaQSjUe1H45bYOuuVaiqzsz5\nF5GZWsVQQ4LKZpupLRnm1HgsrpbMrSwwLZGkUq0hu2sDX/ziz3lvQ4r/fnRX/pEDvYoqlTtN3L+X\nUu47zi9dyBuEEGK+ocdv8jznPTOnnSkXzb40VlXR/Kruo717PZ39W1gy/70YsdHglDKBh8CWnQ/T\nUDuPuurZ+NJn665Hqa6YTsPUk/E0BTOpYyZ0spVRqBHU1Beoa8yXM1QzkoGgmhI3qNDr0c0cm59/\ngev/6jbvlQNDjiHEU8OW++U3Y1/U4XjLiakSQogIcI1qxL6iaJFpU5ZdEmtYfL4aLfZBlY0q1PEC\n6uBLcZA1/mSOVoej1KhdatrWdDlacxrx6Hn5BVINFQhZYPeqJ83dT64Wiqo8ZucLNwPPhJmotwdB\nySqXxJPRLwOnvveaM9UPfPhM46TlreP6pZyiFbrlKVieIOcqZB21HFXtNqG3L8pgf4yRHoPq3hxd\nT/6CJdPejaFPbL9fQkpJV98mIkaK6soWlMM4C/m+T8/ANtp2POD2D+92kGx1vcK3gQdDe/63D0KI\n09Vo9IvS8y6dctrp/rSzL4zXLDwZz9PLQmqs+cTYPqlARAVWvaUSv6C87y5OW/KRw2ZAJ0NKye4D\nz5LOdbNg7qWkZ06hb2qKppYcra0Z6jK97Hvsae+RO57IZzNWXzZrfgf4hZQTDDwJeUsihJgXjRmf\n83z/o/NPW+Ct/MB5qcbTl5EWBumsFgipYo+UzFE2mihlTQ0rcJrEKrB2850smnMpyXjdq96PTK6X\nbbt/T3PjKaTmnloW/9NaM8yZYrGwChZUWSQLJqvu3ypv/f49mb37+z3b8W72JT+SUg4eh5cn5ARE\nCDFFEeqnhVD+ujLVrMxrPa9ixtSVRIzEpH9jO3k27PgNDQ2LaW5aesjvx4qp0s9SStZuuYs5Le+i\nMjUVgB17HyeXH6Ai2UTLzHdix41yhqpQo1NTFwiq5hqH1lQgqOpIs+YPbdzxk2fSa19+RZG+/KFl\nu/9PSrn/2L4yJwZvWTFVohhBXa5G4p+UvvcRo2qKX7vsgmTFgjPU2NRWhBBlsQOjYmnsPJWDBdRr\nElRlxytG3bCwGN65hS2/vMPNdnXZUspe1yz8p+96d0gpj93ggZA3HUKIOZGofoOmKX+pG1pi0ZJm\n44LLTlE/eN0ZJCqTo0LKUUk7CkOWGswsKUBXWmWwL8ZgfxR6JJV9OfY+8VNOW3TtER93YHgvI9lO\nZk17x4S/l9JnYHgv7d3rnF0Hni3YTtaSUv7U9ayfSim3HuvXIeTNgxCiUajqdVo0/mkp/ebGledS\nd8qZsWTrSfhEg2G+xT6pseV9JRFVykxt2Xg382acRzxW/br2p2Bl2Lr7UVLxBhLT5rMzv93v2/FY\nxuzrUHVDu6uQyf8IeDEMVr19EUKkgCvjVakb7YJ10tSVpzkNy9+VjLWeRsGpQs35hwgprWiWIl2H\ndW2/ZMmcy4jHal7zPkgp2d/1Ev1De5g772KsGdPomV5BTWov/oHn5IHHn0vva9sVSSYjjw4OZP8T\neCzs43v7IoQwgEsjRurTrls4p7FugTWz+YyKg0f59A2+wp7O1SxaeAXRSGpcWV+JyX72fY/NOx8i\nEatj5rQzy0Gt/qHd7Ot8iZNOugYnESm7/KWro1TXW6TiPVg7X6b9qRcy25/daCSSkZeGBrI/IAiw\n5nkL85YXU2Mp9qi8W9GNDyKUKxTdiNeefIZWPe+kSOWcBcTqm1DVQwfqTiSeDh5eORFj3a0AfNcl\n17Gb4d3b6N+4Ntu/pU1XNW2PY5p3IeU94UC9kIMpBgNWarp6VTSqX+M4XtPyFTO9y65cEV2wfA71\ns1rIeDo9pkKvCR156B+MlMVUZU8ed8s6lMFBWppOPeLjFaw0qhpBL86iktJnJNNJ39Auuvo2mx09\nGwDZ7/nufb7v3gM8F5afhhyMEGIBQlylRuPX+o49r2LWyXZq3spksnkhlbUzMaQ6cXlf11Zyme6j\nLkedCCkl2Xw/fUOv0NO/3d7f9ZLrupaDEPd7nn0PsOrN6hgVcvwQQjQDH1RjiWt92zot0Ti70Ni6\nIlVXM0fUV84iIoyykMJ1WNf2KxbOvvg1ZaQmIp3rZcO2+zALQ36mMJg1zSFV6Ooqr2DeAzwQmkqE\nHIwQogq4Qtdi13i+c24q3uA0N56SzJmDSm39PObOvXhcdv9g8TTRdWN/7urbQnv3WmZOO7PcC5jO\ndrF19+85ZfHVuJpCd24v3SM7/e79L2XNvv0RLWI86+TzdwP3Siknnir9FuRtJabGUlykLkSIS/RE\n8iLftleiiGhFy8xC1czZydT0Fj05dRrJhnoiVZUomn5UAgrANU0Kw0PkurvJdHQwsm+vObx7VyHT\n2ZnUDKMTIZ51crlHgUeklL3H9YmGvKUQQrQAl1RWJy52He9Mx/Fqmuc055sWzownWqdHlMZpeJEW\n8u4U/HSMyv48Bx67jWVzLkdVJp+E4Hk2pjXC+q33Ulczh+F0hz0wvCc3nG6PC0UZUYT6vO2Uj9k9\nf7InHPKmRwhRB1ykRhMXA+/y7cK0WNXUbGXD7EhV1Yx4ZbKJqmgDMRlhy5Zfs/Kk64+qvM/3XUwr\nTc4cYCTTyUimwx0Y3psdHNln+NJzNNV4yXbyq6T0HwXawgxUyNEihEgA71b16EWK0M5zHXNuPF6b\nq6lq1WoqWpKDQ3uYO+Nc6mvmYOjxoy5H9aWPZaXJmYOks12MZDr9gZG9mcHhvYrtmqquRTc4buEP\nvu8+QpA1DculQ46KYsbqLEXRL9U043zXtZYYRtKqrm6lpmpmqqKiWVQmpxCPVhOJpMo+AhMJrLHX\ne57LrvZn6Bt4BSEEhh6XXX1bLMvO2KaVjup6bKvnO497rrUKePLt2if9thVTEyGEmA4sQ4iFkVRq\npe95C33HafIcp0LRNFeLRDxF14WiKghVlUJRkJ6H7/lCeh6eY0u3YOlIiaJraUXTO4ANTi73ArAV\neDl0iAo5lhQXqqcCC2KVyZWez2LPdps9x64SiuobRtJSfLSokXJUVUdRNCl9D893hOc5wnFNadnZ\niO97mqrqQ4pQ+4WirrfszEsEx+xaKWXPG/ssQ95KFEurlgOLdCOxXAjlFN9zpnueXSsQwjASpqHH\npaoYqKqGouhSSg/Pc4TnO8J1LWnZWd3zbENR9bSq6D2KULZbTm61lP4WYD2wLxRPIceKYg/2UmCx\npkaXq6q2zPfdGZ7v1kjpG4YeNw094auqLlRFl6qiS4kUnufg+45wPVtadlZz3EJUVbSsouh9qqLt\ntN38Gt9324A2YFuY5Q85VgghVGAxcJKq6ks0LbbC953ZvufW+b4b0/RYIWIkPVU1gmNW1aVA4Hm2\n8HxXuJ4lbTunOk4+JhTNVBVtUFG0PZ5nrXU9ex2wGdgYZvkDQjF1FBSNAWqARiAORIEYoAOFMVsG\n6AKy4Rd5yBtJMfNaCUwBkgTHbBSIADZgEhyzOaAbGA6P2ZA3GiFEEmgCUgTn2NJx6zB6ns0DPcBA\nuPgMeaMRQsQI1gY1BOfX0vrAY/z6oA/oDfudQt5ohBA60ADUM3qOjQIKo2uDAjAA9EgprTdoV980\nhGIqJCQkJCQkJCQkJCTkNXDkCbQhISEhISEhISEhISEhhxCKqZCQkJCQkJCQkJCQkNdAKKZCQkJC\nQkJCQkJCQkJeA6GYCgkJCQkJCQkJCQkJeQ2EYiokJCQkJCQkJCQkJOQ18P8D/D0nNHvNIsAAAAAA\nSUVORK5CYII=\n","text/plain":["<Figure size 1080x432 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"EzbVkDAwUmfa","colab_type":"code","outputId":"a2993b3f-7272-41ae-d8c5-b4c987af98f7","executionInfo":{"status":"ok","timestamp":1576425912376,"user_tz":-60,"elapsed":88242,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":84}},"source":["# Accuracy over the testing set\n","predictions = best_model.predict(test_features)\n","predictions = np.round(predictions)\n","\n","score = np.array(best_model.evaluate(test_features, test_labels, verbose=0, sample_weight=test_weights))\n","print(\"Model performance on test set:\\t[ Loss: {}\\tAccuracy: {} ]\".format(*score.round(4)))\n","print(\"\\nPredictions: {}\\nSolutions:   {}\".format(list(map(int, predictions))[:50], list(map(int, test_labels))[:50]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model performance on test set:\t[ Loss: 0.3895\tAccuracy: 0.7731 ]\n","\n","Predictions: [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1]\n","Solutions:   [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DVKxqyKZpyg8","colab_type":"code","outputId":"f861b654-6c1d-4e47-b6e7-6de76541db83","executionInfo":{"status":"ok","timestamp":1576425912660,"user_tz":-60,"elapsed":88514,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":392}},"source":["# Weighted confusion matrix (noP300: 80%, P300: 20%)\n","data = confusion_matrix(y_true=test_labels, y_pred=predictions, sample_weight=test_weights)\n","\n","# Normalized confusion matrix (values in range 0-1)\n","data_norm = data/np.full(data.shape, len(test_labels))\n","\n","# Plot the confusion matrix\n","df_cm = pd.DataFrame(data_norm, columns=np.unique(test_labels), index = np.unique(test_labels))\n","df_cm.index.name = 'Actual'\n","df_cm.columns.name = 'Predicted'\n","plt.figure(figsize = (6,5))\n","sns.set(font_scale = 1.4)\n","cm = sns.heatmap(df_cm, cmap=\"Blues\", annot=True, annot_kws = {\"size\": 16}, vmin=0, vmax=1)\n","cm.axes.set_title(\"CNN2a confusion matrix\\n\", fontsize=20)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAF3CAYAAAC/h9zqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuFxOWKpjInCCu2ecJAcKECIihauDDMLdpwAZL+Tug4BBMdsfIiy3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iEij0Aju9XnJZRTT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\n","text/plain":["<Figure size 432x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"DWOAIheCqbIA","colab_type":"code","colab":{}},"source":["# Model metrics (sens, spec, ppv, npv)\n","def model_metrics(conf_matrix):\n","    tn, fp, fn, tp = list(data_norm.flatten())\n","    sens = round(tp/(tp+fn),4) # Sensitivity\n","    spec = round(tn/(tn+fp),4) # Specificity\n","    ppv = round(tp/(tp+fp),4) # Positive Predicted Value\n","    npv = round(tn/(tn+fn),4) # Negative Predicted Value\n","    return {\"Sensitivity\":sens, \"Specificity\":spec, \"PPV\":ppv, \"NPV\":npv}"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6Wle-089qbTv","colab_type":"code","outputId":"1ae01b2a-3b1b-4f85-f60d-bf27978ec317","executionInfo":{"status":"ok","timestamp":1576425912663,"user_tz":-60,"elapsed":88501,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["# Put model metrics into a table\n","metrics = model_metrics(data_norm)\n","\n","# Create figure\n","fig = plt.figure(figsize=(5,1))\n","ax = fig.add_subplot(111)\n","\n","# Hide graph outlines\n","for item in [fig, ax]:\n","    item.patch.set_visible(False)\n","ax.xaxis.set_visible(False)\n","ax.yaxis.set_visible(False)\n","ax.spines[\"top\"].set_visible(False)\n","ax.spines[\"bottom\"].set_visible(False)\n","ax.spines[\"left\"].set_visible(False)\n","ax.spines[\"right\"].set_visible(False)\n","\n","# Table definition\n","table = ax.table(cellText=[list(metrics.values())], \n","                     colLabels=list(metrics.keys()),\n","                     loc=\"center\",\n","                     cellLoc=\"center\",\n","                     colColours=[\"c\"]*4)\n","table.set_fontsize(16)\n","table.scale(2,2)\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 360x72 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}